1-Compound Fuzzy PID Level Control System Based on WinCC and MATLAB

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ControlLogix系统故障排除与恢复操作指南说明书

Course NumberCCP299Course PurposeUpon completion of this course, you should be able to troubleshoot a previously operational ControlLogix® system and restore normal operation.COURSE AGENDA• Locating ControlLogix Components• Navigating through the Studio 5000 Logix Designer Application• Connecting a Computer to a CommunicationsNetwork• Downloading and Going Online• Locating I/O Tags and DevicesControlLogix/Studio 5000 Studio 5000 Logix Designer Level 1: ControlLogix Fundamentalsand TroubleshootingWHO SHOULD ATTENDThis course is intended for individuals who need to maintain and troubleshoot a ControlLogix system – but have no current working experience with ControlLogix systems.Curriculum Note: This course contains many of thelessons in courses CCP146 and CCP153 – in a consolidated four and a half-day format. Do not take all three courses.PREREQUISITESTo successfully complete this course, the following prerequisites are required:• Ability to perform basic Microsoft Windows tasks • Previous experience with common industrial control system concepts STUDENT MATERIALSTo enhance and facilitate the students’ learningexperiences, the following materials are provided as part of the course package:• Student Manual:–Includes the key concepts, definitions, examples, and activities presented in this course • Lab Book:–Provides learning activities and hands-on practice. Solutions are included after each exercise for immediate feedback.• Logix5000 System Glossary:–Contains terms and definitions specific to Logix5000 systems and defines key Logix5000 systems terminology• Studio 5000 Logix Designer and Logix5000 Procedures Guide:–Provides the steps required to complete basic tasks that are common to all Logix5000 hardware platforms • ControlLogix Troubleshooting Guide:–Contains a systematic approach to diagnosing and troubleshooting common ControlLogix system problems HANDS-ON PRACTICEThroughout this course, you will have the opportunity to practice the skills you have learned through a variety of hands-on exercises using an ABT-TDCLX3-B ControlLogix workstation. Exercises focus on the skills introduced in each lesson.Integrated practices combine and practice several key skills at once.COURSE LENGTHThis is a four-and-a-half-day course.Allen-Bradley, ControlLogix, Logix5000 and Studio 5000 Logix Designer are trademarks of Rockwell Automation, Inc.Trademarks not belonging to Rockwell Automation are property of their respective companies. Publication GMST10-PP335K-EN-E – January 2020 | Supersedes Publication GMST10-PP335J-EN-E – April 2018Copyright © 2020 Rockwell Automation, Inc. All Rights Reserved. Printed in USA.To be respectful of the environment, Rockwell Automation is transitioning some of its training courses to a paperless format. Students are asked tocomplete downloads and bring personal devices to these classes. A full list of digital/paperless courses is currently available through your local distributor.。

华三路由器软件升级指南

8.1 CMW710-R0106P02 版本解决问题列表 ···························································································· 10 8.2 CMW710-R0106 版本解决问题列表··································································································· 11 8.3 CMW710-R0105P12 版本解决问题列表 ···························································································· 12 8.4 CMW710-R0105P06 版本解决问题列表 ···························································································· 13 8.5 CMW710-R0105 版本解决问题列表··································································································· 13 8.6 CMW710-E0104 版本解决问题列表··································································································· 14 8.7 CMW710-E0102 版本解决问题列表··································································································· 14 8.8 CMW710-E0006P02 版本解决问题列表 ···························································································· 14 9 相关资料 ············································································································································· 14 9.1 相关资料清单······································································································································ 14 9.2 资料获取方式······································································································································ 15 10 技术支持 ··········································································································································· 15 附录 A 本版本支持的软、硬件特性列表································································································· 16 A.1 版本硬件特性 ····································································································································· 16 A.2 版本软件特性 ····································································································································· 23

neuro-fuzzy

Combining classifiers of pesticides toxicity through aneuro-fuzzy approachEmilio Benfenati1, Paolo Mazzatorta1, Daniel Neagu2, and Giuseppina Gini21 Istituto di Ricerche Farmacologiche "Mario Negri" Milano,Via Eritrea, 62, 20157 Milano, Italy{Benfenati, Mazzatorta}@ marionegri.it2 Dipartimento di Elettronica e Informazione, Politecnico di Milano,Piazza L. da Vinci 32, 20133 Milano, ItalyNeagu@fusberta.elet.polimi.it, Gini@elet.polimi.ithttp://airlab.elet.polimi.it/imagetoxAbstract. The increasing amount and complexity of data in toxicity predictioncalls for new approaches based on hybrid intelligent methods for mining thedata. This focus is required even more in the context of increasing number ofdifferent classifiers applied in toxicity prediction. Consequently, there exist aneed to develop tools to integrate various approaches. The goal of this researchis to apply neuro-fuzzy networks to provide an improvement in combining theresults of five classifiers applied in toxicity of pesticides. Nevertheless, fuzzyrules extracted from the trained developed networks can be used to performuseful comparisons between the performances of the involved classifiers. Ourresults suggest that the neuro-fuzzy approach of combining classifiers has thepotential to significantly improve common classification methods for the use intoxicity of pesticides characterization, and knowledge discovery.1 IntroductionQuantitative structure–activity relationships (QSARs) correlate chemical structure to a wide variety of physical, chemical, biological (including biomedical, toxicological, ecotoxicological) and technological (glass transition temperatures of polymers, critical micelle concentrations of surfactants, rubber vulcanization rates) properties. Suitable correlations, once established and validated, can be used to predict properties for compounds as yet unmeasured or even unknown.Classification systems for QSAR studies are quite usual for carcinogenicity [9], because in this case carcinogenicity classes are defined by regulatory bodies such as IARC and EPA. For ecotoxicity, most of the QSAR models are regressions, referring to the dose giving the toxic effect in 50% of the animals (for instance LC50: lethal concentration for 50% of the test animals). This dose is a continuous value and regression seems the most appropriate algorithm. However, classification affords some advantages. Indeed, i) the regulatory values are indicated as toxicity classes and ii) classification can allow a better management of noisy data. For this reason we investigated classification in the past [7], [8], [9] and also in this study. No generalrule exists to define an approach suitable to solve a specific classification problem. In several cases, a selection of descriptors is the only essential condition to develop a general system. The next step consists in defining the best computational method to develop robust structure–activity models.Artificial neural networks (ANNs) represent an excellent tool that have been used to develop a wide range of real-world applications, especially when traditional solving methods fail [3]. They exhibit advantages such as ideal learning ability from data, classification capabilities and generalization, computationally fastness once trained due to parallel processing, and noise tolerance. The major shortcoming of neural networks is represented by their low degree of human comprehensibility. More transparency is offered by fuzzy neural networks FNN [14], [16], [18], which represent a paradigm combining the comprehensibility and capabilities of fuzzy reasoning to handle uncertainty, and the capabilities to learn from examples.The paper is organized as follows. Section 2 briefly presents the aspects of data preparation, based on chemical descriptors, some of the most common classification techniques and shows how they behave for toxicology modeling, with a emphasis to pesticides task. Section 3 proposes the neuro-fuzzy approach in order to manage the integration of all the studied classifiers, based on the structure developed as FNN Implicit Knowledge Module (IKM) of the hybrid intelligent system NIKE (Neural explicit&Implicit Knowledge inference system [17]). Preliminary results indicate that combination of several classifiers may lead to the improved performance [5], [11], [12]. The extracted fuzzy rules give new insights about the applicability domain of the implied classifiers. Conclusions of the paper are summarized in the last section.2 Materials and Methods2.1 Data setFor this paper a data set constituted of 57 common organophosphorous compounds has been investigated. The main objective is to propose a good benchmark for the classification studies developed in this area. The toxicity values are the result of a wide bibliographic research mainly from “the Pesticide Manual”, ECOTOX database system, RTECS and HSDB [1]. An important problem that we faced is connected with the variability that the toxicity data presents [2]. Indeed, it is possible to find different fonts showing for the same compound and the same end–point LC50 different for about two orders of magnitude. Such variability is due to different factors, as the different individual reactions of organisms tested, the different laboratory procedures, or is due to different experimental conditions or accidental errors.The toxicity value was expressed using the form Log10 (1/LC50). Then the values were scaled in the interval [-1..1]. Four classes were defined: Class 1 [-1..-0.5), Class 2 [-0.5..0), Class 3 [0..0.5), Class 4 [0.5..1] (Table 2).2.2 DescriptorsA set of about 150 descriptors were calculated by different software: Hyperchem 5.01, CODESSA 2.2.12, Pallas 2.13. They are split into six categories: Constitutional (34 descriptors), Geometrical (14), Topological (38), Electrostatic (57), Quantum–chemicals (6), and Physico–chemical (4). In order to obtain a good model, a selection of the variables, which better describe the molecules, is necessary. There is the risk that some descriptors does not add information, and increase the noise, making more complex the result analysis. Furthermore, using a relatively low number of variables, the risk of overfitting is reduced. The descriptors selection (table 1) was obtained by Principal Components Analysis (PCA), using SCAN4:Table 1. Names of the chemical descriptors involved in the classification task.Cat. Cod.Moment of inertia A G D1Relative number of N atoms C D2Binding energy (Kcal/mol) Q D3DPSA-3 Difference in CPSAs (PPSA3-PNSA3) [Zefirov’s PC] E D4Max partial charge (Qmax) [Zefirov’s PC] E D5ZX Shadow / ZX Rectangle G D6Number of atoms C D7Moment of inertia C G D8PNSA-3 Atomic charge weighted PNSA [Zefirov’s PC] E D9HOMO (eV) E D10LUMO (eV) Q D11Kier&Hall index (order 3) T D122.3 Classification algorithmsThe classification algorithms used for this work are five: LDA (Linear Discriminant Analysis), RDA (Regularized Discriminant Analysis), SIMCA (Soft Independent Modeling of Class Analogy), KNN (K Nearest Neighbors classification), CART (Classification And Regression Tree). The first four are parametric statistical systems based on the Fisher’s discriminant analysis, the fifth and sixth are not parametrical statistical methods, the last one is a classification tree.LDA: the Fischer’s linear discrimination is an empirical method based on p–dimensional vectors of attributes. Thus the separation between classes occurs by an hyperplane, which divides the p–dimensional space of attributes.RDA: The variations introduced in this model have the aim to obviate the principal problems that afflict both the linear and quadratic discrimination. The regulation more efficient was carried out by Friedman, who proposed a compromise between the two previous techniques using a biparametrical method for the estimation (λ and γ).1 Hypercube Inc., Gainsville, Florida, USA2 SemiChem Inc., Shawnee, Kansas, USA3 CompuDrug; Budapest, Hungary4 SCAN (Software for Chemometric Analysis) v.1.1, from Minitab: SIMCA: the model is one of the first used in chemometry for modeling classes and, contrarily to the techniques before described, is not parametrical. The idea is to consider separately each class and to look for a representation using the principal components. An object is assigned to a class on the basis of the residual distance, rsd 2, that it has from the model which represent the class itself:()22ˆigj igj igj x xr −=, )(22j j igjM p r rsd −=∑ (1)where x igj = co –ordinates of the object’s projections on the inner space of the mathematical model for the class, x igj = object’s co –ordinates, p=number of variables, M j = number of the principal components significant for the j class.KNN: this technique classifies each record in a data set based on a combination of the classes of the k record(s) most similar to it in a historical data set (where k = 1). CART is a tree –shaped structure that represents sets of decisions. These decisions generate rules for the classification of a data set. CART provides a set of rules that can be applied to a new (unclassified) data set to predict which records will have a given outcome. It segments a data set by creating two –way splits.The classification obtained using these algorithms is shown in Table 2.2.4 ValidationThe more common methods for validation are: i) Leave –one –out (LOO); ii) Leave –more –out (LMO); iii) Train & Test; iv) Bootstrap. We used LOO, since it is considered the best working on data set of small dimension [10]. According to LOO, given n objects, n models are computed. For each model, the training set consists of n –1 objects and the evaluation set consists of the object left. To estimate the predictive ability, we considered the gap between the experimental (fitting) and the predicted value (cross –validation) for the n objects left, one by one, out from the model.Table 2. True class and class assigned by the algorithms for each compound 5.True Class CART LDA KNN SIMCARDAAnilofos 2 2 2 1 2 2 Chlorpyrifos1 2 2 1 2 2 Chlorpyryfos-methyl 2 2 2 1 2 2 Isazofos 1 1 1 2 1 1 Phosalone 2 2 2 2 2 2 Profenofos 1 2 2 1 2 2 Prothiofos 2 2 2 2 2 2 Azamethiphos 2 2 2 1 4 2 Azinphos methyl 1 1 1 2 1 1 Diazinon 3 3 1 1 4 1 Phosmet2 2 2 1 2 2 Pirimiphos ethyl 1 1 1 1 1 1 Pirimiphos methyl2312115 The 40 molecules with a blank background were used to train the neuro-fuzzy classifier.Pyrazophos 2 2 1 4 2 1Quinalphos 1 1 1 2 1 1Azinphos-ethyl 1 1 1 1 2 1Etrimfos 1 1 1 3 3 1Fosthiazate 4 2 2 2 4 2Methidathion 1 1 1 1 1 1Piperophos 3 3 3 2 2 3Tebupirimfos 4 1 1 3 4 1Triazophos 1 1 1 2 1 1Dichlorvos 2 4 2 2 2 2Disulfoton 3 3 3 1 3 3Ethephon 4 4 4 4 4 4Fenamiphos 1 1 3 2 1 1Fenthion 2 2 3 2 2 3Fonofos 1 1 3 2 1 3Glyphosate 4 4 4 4 4 4Isofenphos (isophenphos) 3 3 3 1 3 3Methamidophos 4 4 4 3 4 4Omethoate 3 3 3 3 3 3Oxydemeton-methyl 3 3 3 3 3 3Parathion ethyl (parathion) 2 2 2 3 1 3Parathion methyl 3 3 3 3 3 3Phoxim 2 2 1 1 1 1Sulfotep 1 1 3 2 2 2Tribufos 2 2 2 2 2 2Trichlorfon 2 2 2 1 2 4Acephate 4 4 1 3 4 4Cadusafos 2 2 3 3 2 2Chlorethoxyfos 2 2 2 3 2 2Demeton-S-methyl 3 3 3 3 3 3Dimethoate 3 3 1 1 3 3Edifenphos 2 2 3 1 2 2EPN 2 2 2 2 2 2Ethion 2 2 2 2 2 2Ethoprophos 3 3 3 2 2 3Fenitrothion 3 2 3 3 3 3Formothion 3 3 2 3 3 3Methacrifos 2 2 2 2 2 3Phorate 1 1 3 2 1 3Propetamphos 3 3 3 4 2 3Sulprofos 3 3 3 2 3 3Temephos 3 3 2 1 3 2Terbufos 1 1 3 2 3 3Thiometon 3 3 3 3 3 33.1 The neuro-fuzzy combination of the classifiers3.2 Motivations and architectureCombining multiple classifiers could be considered as a direction for the development of highly reliable pattern recognition systems, coming from the hybrid intelligent systems approach. Combination of several classifiers may result in improved performances [4], [5]. The necessity of combining multiple classifiers is arising from the main demand of increasing quality and reliability of the final models. There are different classification algorithms in almost all the current pattern recognition application areas, each one having certain degrees of success, but none of them beingas good as expected in applications. The combination technique we propose for the toxicity classification is a neuro-fuzzy gating of the implied classifiers, trained against the correct data. This approach allows multiple classifiers to work together.For this task, the hybrid intelligent system NIKE was used, in order to automate the processes involved, from the data representation for toxicity measurements, to the prediction of toxicity for given new input. It also suggests how the fuzzy inference produced the result, when required [17], based on the effect measure method to combine the weights between the layers of the network in order to select the strongest input-output dependencies [6]. Consequently, for NIKE, we defined the implicit knowledge as the knowledge acquired by neural/neuro-fuzzy nets.Fig. 1. Implicit Knowledge Module implemented as FNN2.The IKM-FNN is implemented as a multilayered neural structure with an input layer, establishing the inputs to perform the membership degrees of the current values, a fully connected three-layered FNN2 [16], and a defuzzification layer [17] (fig.1). The weights of the connections between layer 1 and layer 2 are set to one. A linguistic variable X i is described by m i fuzzy sets, A ij, having the degrees of membership performed by the functions µij(x i), j=1,2,...,m i, i=1,2,..,p., (in our case, p=5, all m i=4, on the classes of the prediction result of the classifiers, as inputs, and on the classes of the toxicity values, as the output y defuz). The layers 1 and 5 are used in the fuzzification process in the training and prediction steps, and the layers 2-4 are organized as a feedforward network to represent the implicit rules through FNN training [15][19]. 3.2 ResultsSince NIKE modules process only data scaled into the interval [0..1], every class was represented by the centroid of each of the four classes in which the available domain was split: 0.135 (class 1), 0.375 (class 2), 0.625 (class 3), and 0.875 (class 4). The inputs and the output followed a trapezoidal (de)fuzzification (fig. 2): VeryLow (0-0.25), Low (0.25-0.5), Medium (0.5-0.75), High (0.75-1).The neuro-fuzzy network was trained on a training set of 40 objects (70% of the entire set, as depicted in Table 2). The training set was used for the adjustment of the connections of the neural and neuro-fuzzy networks with backpropagation (traingdx) algorithm; traingdx is a network training function that updates weight and bias values according to gradient descent momentum and an adaptive learning rate. The neuro-fuzzy network was a multi-layered structure with the 5x4 above described fuzzy inputs and 4 fuzzy output neurons, the toxicity class linguistic variable (fig. 2.a). The number of hidden neurons parameterized the FNN. After different models (5 to 50 hidden units), a medium number of hidden units is desirable and had the same best results: IKM-FNN with 10, 12 and 19 neurons (fig. 3).(a) (b)Fig. 2. NIKE: (a)The fuzzy terms of the generic linguistic variable Class; (b) the FNN model. Table 3. Performances of the classification algorithms computed.NER% fitting NER%validation DescriptorsLDA 64.91 61.40 D1,D2, D3, D4RDA 84.21 71.93 D1, D2, D3, D4, D6, D7, D8, D11, D12, D13 SIMCA 92.98 77.19 D1, D2, D3, D4, D5, D6, D7, D8, D10, D11, D12 KNN - 61.40 D1, D12CART 85.96 77.19 D1, D2, D3, D4, D5, D9Table 4. Confusion matrix of the neuro-fuzzy combination of classifiers.N° of objectsAssigned Class1 2 3 41 132 15True Class2 20 203 1 15 164 6 6Table 5. True class and class assigned by all the classifiers for each compound wrong predicted by the neuro-fuzzy combination of classifiers.True Class CART LDA KNN SIMCA RDA FNN Chlorpyrifos 1 2 2 1 2 2 2Profenofos 1 2 2 1 2 2 2Fenitrothion 3 2 3 3 3 3 2(a)(b)(c)(d)(e)(f)Fig. 3. The results of training FNNs: (a) 3-5 errors, the best are FNN10H, FNN12H and FNN19H; (b) the chosen model, FNN10H, against the SIMCA results and the real ones; (c) the bad fuzzy inference prediction for 2 cases in class 1 (Chlorpyrifos and Profenofos); (d) the bad fuzzy inference prediction for the case in class 3 (Fenitrothion); two samples of good prediction for test cases: (e) a class 1 sample (Phorate); (f) a class 2 sample (Edinfenphos).A momentum term of 0.95 was used (to prevent too many oscillations of the error function). The nets were trained up to 5000 epochs, giving an error about 0.015. The recognition error for the above models is 5.26% (table 4, 5, fig. 3).The confusion matrix shows the ability in prediction of our approach. Looking of Table 3, we notice that the best performance was obtained by SIMCA, which could correctly classify almost 93% of the molecules. This encouraging result was obtained with whole data set involved in developing the model. If we take a look to the NER% validated with LOO, we can notice that we loss a lot of the reliability of the model when we predict the toxicity of an external object. Such a behavior proves the ability in modeling of these algorithms, but shows also their incapacity in generalization. The neuro-fuzzy approach seems to overcome this problem, succeeding in voting for the best opinion and underling all the considered classification algorithms (fig. 3).3.3 Interpreting the results of the neuro-fuzzy combination of the classifiers The most relevant fuzzy rules were extracted from the IKM-FNN structures using Effect Measure Method [6][13]. Finally, after deleting the contradictory rules, the next list of the most trusty fuzzy rules were considered for the chosen net IKM-FNN10H: IF CarFit1 is:VeryLow THEN class is:High (39.22%)IF CarFit1 is:Low THEN class is:High (82.30%)IF CarFit1 is:Medium THEN class is:High (48.74%)IF CarFit1 is:High THEN class is:High (39.04%)IF SimFit1 is:VeryLow THEN class is:Medium (61.25%)IF SimFit1 is:Low THEN class is:Medium (36.04%)IF SimFit1 is:High THEN class is:Medium (43.72%)IF RdaFit1 is:VeryLow THEN class is:Low (75.65%)IF RdaFit1 is:Low THEN class is:Low (100.00%)IF RdaFit1 is:High THEN class is:High (76.39%)Three types of fuzzy rules were obtained: some could be grouped by the same output, or by having the same fuzzy term in the premise and conclusion, and, finally, rules with mixed terms in premises and conclusion parts. From the first two groups of fuzzy rules (italics), we could conclude that, the opinion of the entry classifier is not important for the given output. More precisely, CART prediction for High values of toxicity (class 4) is better to not be taken in consideration.IF (CarFit1 is:VeryLow) OR (CarFit1 is:Low) OR (CarFit1 is:Medium) OR (CarFit1 is:High) THEN class is:HighSimilarly, SIMCA outputs are not so important for predicting class 3 (Medium toxicity: the second group of fuzzy rules). From the second last group of rules, we could find which is the best classifier from the involved systems. In our case, in order to predict class 2 (Low toxicity) is better to consider the opinion coming from RDA. The same opinion is very important for predicting the class 4 (High toxicity) cases too.ConclusionsClassification of the toxicity requires a high degree of experience from computational chemistry experts. Several approaches were described to generate suitable computer-based classifiers for the considered patterns. We investigated five different classifiers and a neuro-fuzzy correlation of them, to organize and classify toxicity data sets. Our approach shown an improved behaviour as a combination of classifiers. Some results viewing fuzzy rules extraction, as well as the possibility to interpret particular inferences suggest that the Neuro-Fuzzy approach has the potential to significantly improve common classification methods for the use in toxicity characterization. AcknowledgmentThis work is partially funded by the E.U. under the contract HPRN-CT-1999-00015. References1. Benfenati, E., Pelagatti, S., Grasso, P., Gini, G.: COMET: the approach of a project in evaluatingtoxicity. In: Gini, G. C.; Katritzky, A. R. (eds.): Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools. AAAI 1999 Spring Symposium Series. AAAI Press, Menlo Park, CA (1999) 40-43 2. Benfenati, E., Piclin, N., Roncaglioni,A., Varì, M.R.: Factors Influencing Predictive Models ForToxicology. SAR and QSAR in environmental research, 12 (2001) 593-603.3. Bishop, C.M.: Neural networks for pattern recognition. Clarendon Press, Oxford (1995)4. Chen, K., Chi, H.: A method of combining multiple probabilistic classifiers through soft competition ondifferent feature sets. Neurocomputing 20 (1998) 227-2525. Duin, R.P.W., Tax, D.M.J.: Experiments with Classifier Combining Rules. Lecture Notes in ComputerScience, 1857, Springer-Verlag, Berlin (2000) 16-296. Enbutsu, I., Baba, K., Hara, N.: Fuzzy Rule Extraction from a Multilayered Network, in Procs. ofIJCNN'91, Seattle (1991) 461-4657. Gini, G., Benfenati, E., Boley, D.: Clustering and Classification Techniques to Assess Aquatic Toxicity.Procs. of the Fourth Int'l Conf. KES2000, Brighton, UK, Vol. 1 (2000) 166-1728. Gini, G., Giumelli, M., Benfenati, E., Lorenzini, P., Boger, Z.: Classification methods to predict activityof toxic compounds, V Seminar on Molecular Similarity, Girona, Spain (2001 forthcoming)9. Gini, G., Lorenzini, M., Benfenati, E., Brambilla, R., Malvé, L.: Mixing a Symbolic and a SubsymbolicExpert to Improve Carcinogenicity Prediction of Aromatic Compounds. In: Kittler,J.,Roli,F.(eds.):Multiple Classifier Systems, Springler-Verlag, Berlin (2001)126-135.10. Helma, C., Gottmann, E., Kramer, S.: Knowledge discovery and data mining in toxicology. Statisticalmethods in medical research, 9 (2000) 131-13511. Ho, T., Hull, J., Srihari, S.: Decision combination in multiple classifier systems. IEEE Trans. PatternAnal. Mach. Intell. 16/1 (1994) 66-7512. Jacobs, R.A.: Methods for combining experts' probability assessments, Neur. Comp. 7/5(1995)867-88813. Jagielska, I., Matthews, C., Whitfort, T.: An investigation into the application of ANN, FL, GA, andrough sets to automated knowledge acquisition for classification problems. Neurocomp,24(1999)37-5414. Kosko, B.: Neural Networks and Fuzzy System. Prentice-Hall, Englewood Cliffs (1992)15. Lin, C.T., George Lee, C.S.: Neural - Network Based Fuzzy Logic Control and Decision System. IEEETransactions on Computers, 40/12 (1991) 1320-133616. Nauck, D., Kruse, R.: NEFCLASS-X: A Neuro-Fuzzy Tool to Build Readable Fuzzy Classifiers. BTTech. J. 16/3 (1998) 180-192.17. Neagu, C.-D., Avouris, N.M., Kalapanidas, E., Palade, V.: Neural and Neuro-fuzzy Integration in aKnowledge-based System for Air Quality Prediction. App Intell. J. (2001 accepted)18. Palade, V., Neagu, C.-D., Patton, R.J.: Interpretation of Trained Neural Networks by Rule Extraction,Procs. of Int'l Conf. 7th Fuzzy Days in Dortmund (2001) 152-161.19. Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing, Explanations in the Microstructureof Cognition. MIT Press (1986)。

使用WinDBG进行双机内核调试

使用WinDBG进行双机内核调试By xikug.xp版本：1.0作者：xIkUg/RCTxikug.xp [at] gmail [dot] com我常去的网站：由于我没有物理的两台机器，因此我这里使用虚拟机来进行讲解(虚拟机真是个好东西)。

我将先讲述如何设置，然后以一个实例来讲述如何进行内核驱动的调试。

Target环境：Virtual PC 2004、Win2000 sp 4 enHost环境：WinXP sp2, WinDBG 6.6.07.5, SUDT SerialNull 1.6 试用版一) 设置篇SUDT SerialNull是一个虚拟串口软件，用于模拟RS232串口的虚拟连接，SerialNull 可以在不占用真实串口的情况下，创建任意数量并互为连接的纯虚拟串口对。

我们将用这个软件虚拟一个串口对出来供Host与Target使用。

如图：我们虚拟了一个串口对，COM2-COM3，这对串口是联通的。

在VirtualPC中设置：进行入虚拟机系统(Win2k)，打开boot.ini文件，添加一行，如下：multi(0)disk(0)rdisk(0)partition(1)\WINNT=”Microsoft Windows 2000 Professional” /fastdetect /debug /debugport=com1 /baudrate=115200意思是以Debug模式起动系统，调试端口为COM1，波特率为115200重新以Debug模式启动系统：在Host端,启动WinDBG，选File->Kernel Debug…，COM页，在BaudRate输入115200，Port输入我们建立的虚拟串口对的COM2口，钩上Reconnected：点击“OK”之后在COMMAND窗口出现：Opened \\.\com2Waiting to reconnect…此时按Ctrl+Break，会出现如下画面：调试器显示：“Connected to Windows 2000 2195 x86 compatible target, ptr64 FALSE”，此时代表连接目标成功，处于中断状态，我们可以在File->Symbol File Path…中设置符号路径，我习惯于使用Symbol Server，因此我在这里设置了：SRV*f:\symbols*/download/symbols到此时我们的双机调试环境搭建完成。

科技英语词汇

科技英语词汇数学absolute value 绝对值acute angle 锐角aggregate集合algebra 代数；代数学algorithm 算法analysis 分析analysis of variance 方差分析analytic function分析函数；解析函数analytic geometry 分析几何analytic number theory 分析数论angle 角angular 角的area 面积arithmetic 算术axiomatic set theory 公理集合论calculus of finite difference 有限差演算calculus of variations 变分法cardinal number 基数category 范畴central limit theorem 中心极限定理circle 圆circular points at infinity 圆点class field theory 类域论classical group 典型群common factor公因数complex function 复变函数complex number 复数cone 圆锥体congruence 同余conjugate function 共轭函数constant 常数convolution 卷积coordinate system 坐标系correlation analysis 相关分析curve 曲线curve of second degree 二次曲线cylinder 圆柱体data anlysis 数据分析decimal 小数decision analysis 决策分析denominator 分母derivative 导数determinant 行列式developable surface 可展曲面differential 微分differential and integral calculus 微积分学differential calculus 微分学differential coefficient 导数differential topology 微分拓扑学dimension 维数divisibility 整除elementary function 初等函数elimination method 消元法ellipse 椭圆elliptic function 椭圆函数entropy 熵equal sign 等号equation 等式；方程式error 误差even number 偶数extract roots开方；求根extremum 极值field 域figure 图形finite field 有限域formula (pl. formulae) 公式function 函数functional 泛函数fuzzy logic 模糊逻辑game theory 博弈论generalized inverse matrix 广义逆矩阵geometry 几何学golden section 黄金分割harmonic function 调和函数hyperbola 双曲线improper integral 广义积分incenter 内心indeterminate 不定方程inequality 不等式infinitesimal 无穷小infinity无穷大integral 积分integral calculus 积分学integral equation 积分方程integration 积分法interval analysis 区间分析limit 极限linear 线性的；一次的linear algebra 线性代数linear operator 线性算子line segment 线段logical calculus 逻辑演算mapping 映射matrix 矩阵maximum function 极大函数minimal surface 极小曲面minus减去；负号model logic 模态逻辑moment矩nomogram 算图normal distribution 正态分布numerator分子numerical analysis 数值分析obtuse angle 钝角odd number 奇数optimization 最优化optimization method 优选学origin 原点parabola 抛物线paradox 悖论parallel 平行；平行线parallel algorithm 并行算法parallelogram平行四边形parameter 参数parity 奇偶性partial derivative 偏导数perpendicular bisector中垂线plane 平面polygon 多边形polyhedron 多面体polynomial 多项式positive sign 正号power 幂probability 概率quadratic 二次的radical sign根号random variable 随机变量real number 实数rectangle 长方形；矩形recursion theory 递归论right angle 直角rotundity圆形semicircle 半圆形series 级数set集；集合side 边simple equation 一次方程式sphere 球体；球面square 正方形；平方；直角尺straight line 直线supplementary 互补surface 曲面symmetry 对称taper 圆锥（形）trapezoid / trapezium 梯形triangle 三角形trigonometry 三角学unknown （未知）元；未知数variable 变量variance变方差vector 向量volume 体积物理absolute zero 绝对零度absorption 吸收acceleration 加速；加速度acoustics 声学activator 激活剂alkaline 碱的；碱性的alloy 合金alternating current (AC) 交流电ampere 安培ampere-meter 安培计annealing 退火antiparticle 反粒子atom原子atomic原子的atomic beam 原子束atmosphere 大气层beam splitter 分光膜boson 玻色子calorie 卡calorimetry 量热术cell 电池Celsius temperature 摄氏温度centripetal force 向心力centre of gravity 重心centre of mass 质心centrifugal force 离心力charge 电荷coating 涂层；覆盖collision 碰撞collider 对撞机compass 指南针conservation of energy能量守恒constraint 约束continuum 连续体；连续介质convex 凸起的cosmic ray 宇宙射线coulomb 库伦coupling 耦合critical state 临界状态cross section 截面crystal 晶体crystallization 结晶crystallography 晶体学cyclotron 回旋加速器damping 阻尼decay 衰变diamagnetism 抗磁性dielectrics 电介质；绝缘体diffraction 衍射diffusion 扩散direct current (DC) 直流电discharge 放电dislocation 位错dispersion 色散displacement 位移distortion 畸变divergence发散Doppler effect 多普勒效应drag 阻力drift 漂移dynamics 动力学eddy current 涡电流elastic force 弹性力elasticity 弹性力学electromagnetic 电磁的electret 驻极体electric circuits 电路electric current 电流electric field 电场electricity 电学electric polarization 电极化electric potential 电位electric power 电功率electroacoustics 电声学electrocaloric effect 电热效应electrolytic 电解的electromagnetic 电磁的electromagnetic induction 电磁感应electromagnetic radiation 电磁辐射electromagnetic shielding 电磁屏蔽electromagnetic wave 电磁波electromagnetism 电磁学electron 电子electroscope 验电器electrostatic field 静电场elementary particle 基本粒子energy 能量enthalpy 焓；热函entropy 熵exciton 激子；激发子farad 法拉ferroelectricity 铁电性fiber optics 纤维光学first cosmic velocity第一宇宙速度fission裂变fluctuation 波动fluid mechanics 流体力学fluorescence 荧光；荧光性force 力free nergy 自由能friction 摩擦fusion聚合galvanometer 电流计gaseous discharge 气体放电generator 发电机；发生器gluon 胶子grating 光栅gravitational interaction 引力相互作用graviton 引力子gravity wve 重力波hadron 强子heat transfer 热传递heavy lepton 重轻子helium 氦holography 全息摄影术humidity 湿度hydrogen 氢hyperon 超子impulse 冲量incandescent lamp 白炽灯inductance 电感inertia 惯性inertial force 惯性力infrared ray 红外线insulator绝缘体ion 离子ionic离子的ionize 电离；使离子化isotope 同位素jet 喷注joule 焦耳kinetic energy 动能laser 激光latent heat 潜热lever 杠杆lens 透镜magnet 磁体；磁铁magnetic field 磁场magnetics 磁学magnetization 磁化（强度）mass 质量meson 介子microscope 显微镜molecule 分子moment 力矩momentum 动量multimeter 万用（电）表neucleon 核子neucleus 原子核neutrino 中微子neutron 中子nuclide 核素ohm欧姆ohmmeter欧姆计optics 光学ozone 臭氧层parity 宇称phase 相phosphor 荧光粉photon 光子plasma 等离子体pulley 滑轮pyrometer高温计quark 夸克quartz 石英quenching 淬火reacting force反作用力radar 雷达radiation 辐射radioactivity 辐射性raodiocarbon dating 碳定年recoil反冲reflection 反射resistance 电阻resonance 共振（态）reverberation 混响rolling 轧制screw 螺旋semi-conductor 半导体shock wave 激波；冲击波soild 固体sonar 声呐spectrum 光谱spin 自旋stress 应力superconductivity 超导电性swing振幅synchronizer 同步装置telescope 望远镜temperature 温度tension 张力transistor 晶体管ultrasonic 超声的ultraviolet ray 紫外线universal gravitation万有引力uranium 铀vacuum 真空velocity 速度vibration 振动viscosity 粘性急volt 伏特voltage 电压voltmeter 伏特计vortex 涡旋weld 焊接work 功X ray X射线化学acid 酸absorbate 吸附质aerosol 气溶胶alkali 碱alkaline 碱（性）的amino acid 氨基酸anode 阳极base 碱boiling point 沸点bubble point 泡点calorimetric entropy量热熵capillarity 毛细现象carbonification 碳化作用catalyst催化剂cathode 阴极chemical affinity 化学亲合势chemical potential 化学势clone 克隆（无性系繁殖）compound 化合物composite reaction复合反应condensation in capillary 毛细管凝结condensed state 凝聚态conductivity 电导率conductance电导consecutive reaction连串反应coulometer电量计；库伦计critical parameter临界参数cyclic process 循环过程decomposition voltage分解电压demulsification 胶凝作用dew point露点dispersion phase 分散相electrode potential 电极电势electrolytic cell 电解池electromotive force 电动势electrophoresis 电泳embryo 胚胎energy level能级entropy 熵enzyme 酶equilibrium state平衡态ethane 乙烷ethanol 乙醇eutectic point 低共熔点excess pressure 附加压力extensive property 广延性质ferment 发酵；酵素freezing point凝固点gelatin凝胶gene 基因genome 基因组half cell 半电池heat of dissociation 离解热heat of neutralization 中和热heat pump 热泵ionic strength离子强度internal energy内能intermolecular force 分子间力latent heat 潜热macromolecular solution高分子溶液mechanical equivalent of heat热功当量metabolism 新陈代谢methane 甲烷microstate 微态molecular distillation 分子蒸馏negative pole 负极negative adsorption 负吸附overheated liquid 过热液体oxidation 氧化（作用）oxidation-reduction 氧化还原partial pressure 分压pascal 帕斯卡phase change 相变photoreaction 光反应photosensitized reaction 光敏反应photosynthesis 光合作用polarization极化作用polyelectrolyte 聚（合）电解质polyethylene 聚乙烯polymer 聚合物rectify精馏reduced temperature 对比温度relative viscosity 相对粘度relative volatility 相对挥发度reversible process 可逆过程salting out 盐析saturated vapor饱和蒸气sedimentation 沉降solid phase line 固相线solid solution 固态混合物solution 溶液straight chain reactions 单链反应surface excess表面吸附量thermodynamics 热力学transgenic 转基因的triple point 三相点unimolecular reaction单分子反应vaporization 气化work content 功函yield 产率计算机专业access arm 磁头臂；存取臂access time 存取时间adder 加法器address 地址alphanumeric 字母数字的analog computer 模拟计算机analyst 分析员area 区域array 数组；阵列ASCII 美国信息交换标准码assembler 汇编程序audio音频band 区band width带宽batch processing 成批处理BBS 电子布告栏系统binary code 二进制码binary digit 二进制位；二进制数字bit 比特,二进制的一位branch 分支,支线browser 浏览器brush 电刷buffer storage 缓冲存储器byte 字节calculator 计算器call instruction 呼叫指令cancel 取消card punch 卡片穿孔机card reader 卡片阅读机；读卡机cell 单元channel 通道；信道character 字符check digit 校验数位chip 芯片circuit 电路；线路click 点击clear 清除；清零clock 时钟code 代码；编码coder 编码员；编码器command 指令；命令compact disk (CD)光盘compatible兼容的compatibility兼容性compiler 编译程序computer language 计算机语言control unit 控制器core storage, core store 磁心存储器counter 计数器CPU 中央处理器cybernetics 控制论cycle 循环cursor 光标data 数据data processing 数据处理debugging 调试decision 制定delete 删除desktop桌面display 显示屏dialog box 对话框digit 数字,数位,位digital computer 数字计算机disc, disk 磁盘display unit 显示装置driver驱动器drop-down menu 下拉菜单edit 编辑EMS memory 内存encode 编码erase 擦除；清洗；抹除feed 馈送；供给feedback 反馈field 字段；信息组,域file 文件fire wall 防火墙floppy disk软盘flush left 左对齐folder 文件夹font 字体format 格式frame 帧hack 黑客hard disk 硬盘help 帮助highlight 突出显示icon图标identifier 标识符index 索引information 信息inline processing 内处理input 输入inquiry 询问insert 插入interactive 交互式instruction 指令item 项目；项jump 转移key 键,关键码keyboard 键盘latency time 等待时间library 库,程序库linkage 连接line spacing single 单倍行距load 装入；寄存；写入；加载location 存储单元logger 登记器,记录器log in/on注册；登录loop 循环machine language 机器语言magnetic storage 磁存储器magnetic tape 磁带main frame主机matrix 矩阵memory 存储器menu 菜单message 信息；报文microcomputer 微型计算机module 组件；模块modify 修改monitor 监视器；监督程序；管程motherboard主板mouse 鼠标multimedia 多媒体nanosecond 毫微秒network 网络；网numeric, numerical 数字的；数值的octet 八位位组；八位字节operator 操作员optical character reader 光符阅读机optical scanner 光扫描器output 输出overflow 溢出；上溢panel 平板parameter 参数；参量perforator 穿孔机peripheral equipment 外围设备；外部设备personal computer 个人计算机printed circuit 印制电路printer 打印机printout 打印输出process 处理processor 微处理器processing unit 处理部件program 程序program 程序编制programmer 程序设计员programming 程序设计；程序编制punch 穿孔punch 穿孔punched card, punch card 穿孔卡片punch hole 孔；穿孔punched tape, punch tape 穿孔纸带random access 随机存取read 读取reader 阅读程序reading 阅读read-only file 只读文件real time 实时record, register 记录redundancy 冗余right-click 右击routine 例行程序selector 选择器,选择符sentinel 标记sequence 序列,顺序sequential 顺序的serial 串行的.连续的server 服务器shift 移位,移数signal 信号simulation 模拟simulator 模拟器；模拟程序software 软件；软设备sort 分类,排序sorter 分类人员；分类机；分类程序；排序程序sound box 音箱storage 存储器store 存储subroutine, subprogram 子程序switch 开关symbol 符号symbolic language 符号语言system 系统table 表格tabulator 制表机teleprinter 电传打字机terminal 终端terminal unit 终端设备timer 时钟；精密计时器time sharing 分时timing 定时toolbar 工具按钮track 磁道transducer 传感器；翻译机translator 翻译程序：翻译器tools 工具update保更新view 视图virus 病毒visual 视频window 窗口经贸acceptance 承兑acquisition 收购ad valorem duty 从价税after-sales service 售后服务amortization 分期偿还（欠款本息）anti-dumping 反倾销appreciation 升值arbitration 仲裁assessment 估价auction 拍卖auditing 审计average 海损bad debt 呆帐bail out 财政援助balance of payment 国际收支差额balance sheet 资产负债表bank credit 银行信贷bankrupt 破产bar chart 柱形图bar code 条形码bargain 讨价还价benchmark 基准beneficiary 受益人bill of entry 报关单bill of exchange 汇票bill of lading 提单blue chip 蓝筹股board 董事会bonded warehouse 保税仓库bonus 红利brainstorming 集思广益brand loyalty 品牌忠诚度break-even point 收支相抵点broker 经纪人budget 预算bulk goods 散装货byproduct 副产品telegraphic transfer 电汇capital gains 资本收益capital stock 股本carriage 运费certificate of origin 原产地证明chamber of commerce 商会claim 索赔commission 佣金consignee 收货人consumer durables 耐用消费品customize 定制dealer 经销商debit card 借记卡decision-making 决策declaration of income 收入申报deficit 赤字deflation 通货紧缩delivery note 交货单differentiation 产品差异化distribution 分销diversification 经营多样化Dow Jones Industrial Average 道琼斯工业平均指数down payment 首付；定金economic indicator 经济指数endorsement 背书enquiry 寻盘exchange control 外汇管制face value 面值floating rate 浮动汇率foreign exchange 外汇franchise 特许经营freelance 自由职业者free on board 离岸价futures market 期货市场gross domestic/national product 国内/国民生产总值hedging 套期保值idle money 闲置资金import licence 进口许可证industrial tribunal 劳资仲裁庭inflation 通货膨胀infrastructure 基础设施insurance policy 保险单interest rate 利率investment trust 投资信托公司joint venture 合资公司legal expense 诉讼费用letter of credit 信用证liquidation 清算management buyout 管理层够入全部股权marginal cost 边际成本marine insurance 海运保险marketing mix 营销组合market segmentation 市场细分merger 合并mortgage 抵押贷款net asset value 净资产值offer 报盘off-season 淡季的option 期权order 定单overdraft 透支overhead 经常费用patent 专利payoff 回报，赢利performance 业绩price discrimination 价格歧视portfolio 投资组合product life cycle 产品生命周期promotion 促销public relations 公共关系quotation 报价rate of returns 收益率rationalization 合理化改革real estate 房地产refund 退款retail price 零售价securities 证券stock exchange 股票交易所subsidy 补贴surplus 过剩tariff 关税trade deficit 贸易赤字transactions velocity of circulation 货币流通速度underwriter 承保人value added tax 增值税医学albomycin 白霉素allergen过敏原allergy过敏allergic reaction 过敏反应allergic rhinitis过敏性鼻炎anaphylactic shock过敏性休克anatomy解剖学anemia贫血anorexia厌食症apoplexy 中风arthritis 关节炎beriberi脚气病blood pressure 血压blood test 验血blood type A A血型；A型血brainwave脑波bronchitis 支气管炎cancer癌症cerebral apoplexy 脑溢血cholera霍乱circulatory system循环系统clinic 诊所color blindness 色盲common cold感冒computerized tomography CT扫描contraceptive避孕用具cough咳嗽dentin牙质dentist 牙科医生dermatologist 皮肤科医生detoxification解毒作用diabetes 糖尿病dialysis 透析diarrhea 痢疾dissection解剖eardrum / tympanic membrane鼓膜electrocardiogram (ECG) 心电图electroencephalogram (EEG) 脑电图ENT (ear-nose-throat) doctor 耳鼻喉科医生epilepsy癫痫erythromycin 红霉素gastric ulcers 胃溃疡glucose葡萄糖gynaecologist 妇科医生gynecology妇科学head nurse 护士长heat stroke 中暑hormone激素house surgeon 住院外科医生hospitalization 住院治疗immune system免疫系统infectious disease 传染病infertility不孕injection 注射in-patient住院病人in-patient department 住院部intern 实习医生in vitro fertilization试管内受精leukemia白血病life expectancy预期寿命lymph淋巴malnutrition营养不良me asles 麻疹migraine偏头痛nutrition营养obesity 肥胖症obstetrician 产科医生oculist 眼科医生oligocardia 心动徐缓oligoocholia 胆汁过少oligospermia 精子减少oligopnea 呼吸迟缓oligosideremia 血铁减少oligochromimia 血红蛋白过少oligocythmia 红细胞减少oligosteatosis 皮脂减少oncologist 肿瘤科医生ophthalmologist眼科专家ophthalmology眼科学ophthalmic眼炎orthopedist 骨科医生out-patient 门诊病人out-patient department 门诊部ovulation排卵paediatrician 儿科医生paralysis 瘫痪penicillin 青霉素perspiration排汗plastic surgeon 整形外科医生pneumonia 肺炎radiologist 放射科医生register / registration 挂号rejection排斥反应resident physician 住院内科医生resistance抵抗力rheumatoid arthritis类风湿性关节炎saturated fat饱和脂肪scarlatina 猩红热scurvy坏血病sinus窦sinusitis鼻窦炎skin test 皮试smallpox 天花sphygmomanometer 血压计stethoscope听诊器streptomycin 链霉素student nurse实习护士syphilis梅毒total lung capacity总肺活量transplant operation 移植手术tuberculosis (TB) 结核病tumor肿瘤typhoid fever伤寒ultrasonic diagnosis B B超urinalysis 尿检urologist 泌尿科医生vaccine 疫苗venous injection 静脉注射vomit 呕吐ward 病房生物学chrom颜色chromophore生色团chromosome染色体chromatography色谱法melan, melano, nigr 黑melanoma黑素瘤melanin黑色素melanophore黑色素细胞xantho, flavo, fla, flavi, lute黄xanthophyl叶黄素xanthous黄色的，黄色人种xathine黄嘌呤flavin(e)黄素flavone黄酮letein黄体素，叶黄素flavin adenine dinucleotide(FAD)黄素腺嘌呤二核苷酸erythro, rub, rubrm, ruf 红erythrocyte红细胞erythromycin红霉素erythropoitin(EPO)促红细胞生成素chloro, chlor绿，氯chlorophyll叶绿素chloride氯化物chloramphenicol氯霉素cyan, cyano 蓝，青紫色，氰cyanophyceae 蓝藻纲cyanobacteria蓝细菌cyanide氰化物aur, glid, chrys金色aureomycin金霉素chrysose 金藻淀粉chrysanthemum菊花glidstone 金沙石glid 镀金leu, leuco, leuk, leuko, blan, alb无色，白色leucine亮氨酸albomycin白霉素cephal, capit, cran 头，头颅cyte 细胞carn, my, mya, myo,肉，肌肉haem, haemat, hem, aem, sangul 血soma, corp 体，身体some, plast 体，颗粒hepa, hepat 肝heparin 肝素hepatopancreas肝胰腺hepatocyte 肝细胞hepatoma肝癌ren, nephr 肾adrnal肾上腺的nephridia肾管nephron肾单位card, cord 心cardiotoxin 心脏毒素cardiovascular center 心血管中枢electrocardiogram心电图concord一致，和谐ophthalm, ocell, ocul 眼bronchi 鳃filibranch丝鳃lamellibrnch瓣鳃sencondary branchium次生鳃brac, brachi 腕，手臂brachiolaria 短腕幼虫brachionectin臂粘连蛋白bracelet手镯dent, odont 牙齿dentin牙质odontphora 齿舌odontoblast成牙质细胞plum羽plumatus 羽状的plumule绒毛plumage （鸟的）羽毛foli, foil 叶follicle滤泡foiling叶形foliage 叶子foliose 多叶的Haplodermatitis 单纯皮炎haplolichen 单纯苔藓haploid 单倍体Haplomycosis 单孢子囊菌病pan agglutination 全凝集ultra micron 超微粒ultrasonic 超声波的ultraviolet 紫外线ultrasound 超声Subabdominal 腹下的subarchinoid 蛛网膜下腔的subaural 耳下的subscapular 肩胛下的subcapduloperiosteal 关节囊骨膜下的subclavicular 胸骨下的substratum 下层hypoderm 皮下组织cranium颅cranial bone颅骨cranial cavity 颅腔hip 髋coax bone / hip bone 髋骨hip girdle 髋带hip replacement 髋置换base pair盐基对base盐基hydrochloric acid 盐酸salts 盐类tooth decay龋齿dental caries 龋齿palatine bone 腭骨patella髌骨pulp cavity 髓腔medulla 髓质medulla 髓质myelin 髓磷脂cellulose纤维素fibrin 纤维蛋白fibrinogen 纤维蛋白原fibrous cartilage纤维软骨农业land, soil 土壤arable land, tilled land 耕地dry soil 旱田fertile soil 沃土，肥沃的土壤humus 腐殖质irrigable land 水浇地lean soil, poor soil 贫瘠土壤wasteland, barren land 荒地grassland 草地meadow 草甸prairie 大草原pasture land 牧场fallow 休闲地stubble, stubble field 亩茬地straw, hay 稿杆rural population 农村人口rural exodus 农村迁徙land reform, agrarian reform 土地改革mechanization of farming 农业机械化mechanized farming 机械化耕作cattle farm 奶牛场ranch 大农场，牧场hacienda 庄园holding 田产plot, parcel, lot 地块cooperative farm 合作农场collective farm集体农场country, countryside 农村countryman 农民，农夫countrywoman 农民，农妇agronomist 农学家latifundium, large landed estate 大农场主farmer 农户producer 农业工人landowner 地主，土地拥有者absentee landlord 外居地主smallholder, small farmer 小农rancher 牧场主tenant farmer, leaseholder 土地租用人sharecropper 佃农ploughman 农夫，犁田者farm labourers 农场工人，农业工人（美作：farm laborers）farm hand 农场短工cattle farmer 牧场工人cowherd, cowboy 牛仔shepherd 牧人fruit grower 果农vinegrower 葡萄栽植者vintager 采葡萄者farming, husbandry 农业animal husbandry, animal breeding 畜牧业dairy farming 乳品业，乳牛业horticulture 园艺market gardening 商品蔬菜种植业fruit growing 果树栽培vinegrowing, viticulture 葡萄栽培olive growing 油橄榄栽培arboriculture树艺silviculture 造林学agricultural products, farm products 农产品foodstuffs 食品dairy produce, dairy products 乳制品dairy industry 乳品加工业crop year, farming year农事年season 季节agricultural, commodities market 农业市场livestock 牲畜alfalfa 紫苜蓿apple 苹果apricot 杏子aquiculture 水产养殖asparagus 芦笋banana 香蕉barley 大麦bean 豆bee-keeping 养蜂beeswax 蜂蜡branch 树枝breed 繁殖，生育，饲养buffalo 水牛，野牛cabbage 洋白菜calf 小牛，仔camel 骆驼carp 鲤鱼carrot 胡萝卜cassava 木薯castor—bean 蓖麻籽castor—O¨蓖麻油cat fish 鲇鱼cattle 牛(总称)cauliflower 菜花chemical fertilizer 化学肥料cherry 樱桃chicken 小鸡chlorophyll 叶绿素cock 公鸡coconut 椰子cocoon 蚕茧cod 鳕鱼colony 蜂群compost 堆肥，混合肥料corn 玉米cotton 棉花COW 母牛cowboy 牛仔crop 农作物cross—breeding 杂交dairy farm 奶牛场desert 沙漠donkey 驴duck 鸭子egg 鸡蛋fertilizer 肥料fiber 纤维fig 无花果fish 鱼forest 森林fruit 水果game bird 可猎取的鸟garlic 大蒜gene—altered food(crop) 转基因食物(作物) gene—engineered food(crop) 转基因食物(作物)ginseng 人参GM(gene—modified) food(crop) 转基因食物(作物)goat 山羊graft 嫁接grain 谷物grape 葡萄hay 干草hen 母鸡herbicide 除草剂hive 蜂箱honey 蜂蜜honeycomb 蜂巢horse 马incubate 孵化insect pest 病虫，害虫insecticide 杀虫剂irrigation 灌溉lamb 羔羊leaf 树叶levee 大堤，堤livestock (总称)牲畜lobster 龙虾locust 蝗虫log 圆木．1umbering 伐木maize 玉米mating 交配milk 牛奶mink 水貂mule 骡mushroom 蘑菇mustard 芥末nectar 花蜜nitric acid 硝酸oat 燕麦onion 洋葱orange 广柑，橙子organic fertilizer 有机肥料ox 牛parched field 焦干的土地peach 桃peanut 花生persimmon 柿子pesticide 杀虫剂pet 供玩赏的动物，爱畜，宠物photosynthesis 光合作用pineapple 菠萝plum 李子pollen 花粉potato 土豆poultry farming 养鸡场queen wasp 蜂王raise 饲养ram 公羊ranch 牧场rattan 藤reservoir 水库root 树根royal jelly 王浆seed 种子silage 青贮饲料silk 丝silkworm 蚕soybean 大豆squash 南瓜stem 茎，树干straw 稻草strawberry 草莓sugarcane 甘蔗tangerine 红皮桔till 耕作tillable 可耕作的tillage 耕作tomato 西红柿trawler 拖网渔船turkey 火鸡vegetable 蔬菜wasp 黄蜂watermelon 西瓜weed 杂草well 井wheat 小麦石油化工oil field 油田wildcat 盲目开掘的油井percussive drilling 冲击钻探rotary drilling 旋转钻探offshore drilling 海底钻探well 井，油井derrick 井架Christmas tree 采油树crown block 定滑轮travelling block 动滑轮drill pipe, drill stem 钻杆drill bit钻头roller bit 牙轮钻头diamond bit 钻石钻头swivel 泥浆喷嘴turntable, rotary table 轮盘pumping station 泵站sampling 取样sample 样品，样本core sample 矿样storage tank 储油罐pipeline 油管pipe laying 输油管线oil tanker 油轮tank car, tanker （铁路）罐车，槽车tank truck, tanker （汽车）运油罐车，油罐车refining 炼油refinery 炼油厂cracking 裂化separation 分离fractionating tower 分馏塔fractional distillation 分馏distillation column 分裂蒸馏塔polymerizing, polymerization 聚合purification 净化hydrocarbon 烃，碳氢化合物crude oil, crude 原油petrol 汽油（美作：gasoline）LPG, liquefied petroleum gas 液化石油气LNG, liquefied natural gas 液化天然气octane number辛烷数，辛烷值vaseline 凡士林paraffin 石蜡kerosene, karaffin oil 煤油gas oil 柴油lubricating oil 润滑油asphalt 沥青benzene 苯fuel 燃料natural gas 天然气olefin 烯烃high-grade petrol 高级汽油plastic 塑料机械assembly line组装线layout布置图conveyer流水线物料板rivet table拉钉机rivet gun拉钉枪screw driver起子electric screw driver电动起子pneumatic screw driver气动起子worktable 工作桌OOBA开箱检查fit together组装在一起fasten锁紧(螺丝)fixture 夹具(治具)pallet栈板barcode条码barcode scanner条码扫描器fuse together熔合fuse machine热熔机repair修理QC品管cosmetic inspect外观检查inner parts inspect内部检查thumb screw大头螺丝lbs. inch镑、英寸EMI gasket导电条front plate前板rear plate后板chassis 基座bezel panel面板power button电源按键reset button重置键hi-pot test of SPS高源高压测试voltage switch of SPS 电源电压接拉键sheet metal parts 冲件plastic parts塑胶件SOP制造作业程序material check list物料检查表work cell工作间trolley台车carton纸箱sub-line支线left fork叉车planning department企划部QC Section品管科stamping factory冲压厂painting factory烤漆厂molding factory成型厂common equipment常用设备uncoiler and straightener整平机punching machine 冲床robot机械手hydraulic machine油压机lathe车床planer 'plein?刨床miller铣床grinder磨床driller铣床linear cutting线切割electrical sparkle电火花welder电焊机staker=reviting machine铆合机general manager总经理be put in storage入库pack packing包装to apply oil擦油to file burr 锉毛刺final inspection终检to connect material接料to reverse material 翻料wet station沾湿台Tiana天那水cleaning cloth抹布to load material上料to unload material卸料to return material/stock to退料scraped 'skr?pid报废scrape ..v.刮;削deficient purchase来料不良manufacture procedure制程deficient manufacturing procedure制程不良oxidation ' ksi'dei?n氧化scratch刮伤dents压痕defective upsiding down抽芽不良defective to staking铆合不良embedded lump镶块feeding is not in place送料不到位stamping-missing漏冲production capacity生产力education and training教育与训练proposal improvement提案改善spare parts=buffer备件forklift叉车trailer=long vehicle拖板车compound die合模die locker锁模器pressure plate=plate pinch压板bolt螺栓automatic screwdriver电动启子thickness gauge厚薄规gauge(or jig)治具power wire电源线buzzle蜂鸣器defective product label不良标签identifying sheet list标示单screwdriver holder起子插座pedal踩踏板stopper阻挡器flow board流水板hydraulic handjack油压板车forklift叉车pallet栈板band-aid创可贴iudustrial alcohol工业酒精alcohol container沾湿台sweeper扫把mop拖把vaccum cleaner吸尘器rag 抹布garbage container灰箕garbage can垃圾箱garbage bag垃圾袋chain链条jack升降机production line流水线chain链条槽magnetizer加磁器lamp holder灯架to mop the floor拖地to clean the floor扫地to clean a table擦桌子air pipe 气管packaging tool打包机packaging打包missing part漏件wrong part错件excessive defects过多的缺陷critical defect极严重缺陷major defect主要缺陷minor defect次要缺陷not up to standard不合规格dimension/size is a little bigger尺寸偏大(小) cosmetic defect外观不良slipped screwhead/slippery screw head螺丝滑头slipped screwhead/shippery screw thread滑手speckle斑点mildewed=moldy=mouldy发霉rust生锈deformation变形burr(金属)flash(塑件)毛边poor staking铆合不良excesssive gap间隙过大grease/oil stains油污shrinking/shrinkage缩水mixed color杂色scratch划伤poor processing 制程不良poor incoming part事件不良fold of pakaging belt打包带折皱painting make-up补漆discoloration羿色water spots水渍polishing/surface processing表面处理exposed metal/bare metal金属裸露lack of painting烤漆不到位delivery deadline交货期cost成本engineering工程die repair模修die worker模工to start a press开机classification整理regulation整顿cleanness清扫qualified products, up-to-grade products良品defective products, not up-to-grade products 不良品waste废料board看板feeder送料机sliding rack滑料架defective product box不良品箱die change 换模to fix a die装模to take apart a die拆模to repair a die修模packing material包材plastic basket胶筐isolating plate baffle plate; barricade隔板carton box纸箱to pull and stretch拉深to put material in place, to cut material, to input落料to impose lines压线to compress, compressing压缩character die字模to feed, feeding送料transportation运输。

永磁同步电动机自适应模糊控制方法的研究【范本模板】

永磁同步电机自适应模糊控制方法的研究
1
1绪论
1。1 课题意义与目的
1。1。1 课题意义
永磁同步电动机英文名称：permanent magnet synchronous motor 定义：采用永磁磁极转子的同步电动机。
对节能要求高的场合:在工农业生产中，有大量的生产机械要求连续地以大致不变的速度运行，例如风机、泵、压缩机、普通机床等.这类机械大量采用三相感应电动机驱动，但感应电动机的效率和功率因数较低,采用异步起动永磁同步电动机可获得高效率和高功率因数。在某些场合，负载率低，若采用三相感应电动机，轻载时功率因数和效率低,经济运行范围窄,造成大量的电能浪费。若采用异步起动永磁同步电动机，可以实现高效、高功率因数和宽广的经济运行范围，节约大量电能。
文中首先概要性介绍了交流调速系统的发展,d—q 坐标系下永磁同步电动机的数学模型，然后建立了永磁同步电机的矢量控制系统。当采用传统的 PI 控制器时，控制器参数与对象匹配的情况下可以取得良好的控制效果。但是当对象参数发生变化时，PI 参数需要重新整定。模糊控制具有不依赖于对象的数学模型、鲁棒性强的优点，能够很好地克服系统中模型参数变化和非线性等不确定因素，从而实现系统的高品质控制。本文将模糊控制与传统 PI 控制器相结合应用于永磁同步电动机调速控制系统中,设计了基于模糊自适应 PI 控制器,用 MATLAB\SIMULINK 进行了仿真，仿真结果表明，这种复合的模糊自适应 PI 控制器较单一的传统 PI 控制器能够获得较好的控制效果。
Firstly the development of AC speed regulation system, the control strategies used in the PMSM control system and the mathematics model of PMSM are generalized in this thesis。 Then， PMSM vector control system is set up. Good performance can be achieved when the PI controller's parameters match with the control system。 However， the parameters of PI have to be modified when the system’s parameters change. Fuzzy control has the advantage of not relying on the object mathematical model and strongly robustness so it can overcome the uncertainty of element in the system such as parameter change and non—linear change and can realize the high quality control performance of the system。 Fuzzy control combined with PI control is applied in the PMSM control system. The simulation results under MATLAB/SIMULINK environment prove that better performance can be obtained by using the compound controller than PI controller.

IEEETransactionsonSmartGrid

MARCH2012VOLUME3NUMBER1ITSGBQ(ISSN1949-3053)REGULAR PAPERSHierarchical Fuzzy Logic System for Implementing Maintenance Schedules of Offshore Power Systems................. .................................................................................C.S.Chang,Z.Wang,F.Yang,and W.W.Tan3 Investigation of Economic and Environmental-Driven Demand Response Measures Incorporating UC.................... ......................................A.Abdollahi,M.Parsa Moghaddam,M.Rashidinejad,and M.K.Sheikh-El-Eslami12 Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints........................... ....................................................................................................O.Sundström and C.Binding26 A Controlled Filtering Method for Estimating Harmonics of Off-Nominal Frequencies..................................... ........................................ C.A.G.Marques,M.V.Ribeiro,C.A.Duque,P.F.Ribeiro,and E.A.B.da Silva38 Coordinated Energy Cost Management of Distributed Internet Data Centers in Smart Grid................................. ...............................................................................................L.Rao,X.Liu,L.Xie,and W.Liu50 Wide-Area Measurement Based Dynamic Stochastic Optimal Power Flow Control for Smart Grids With High Variabilityand Uncertainty.......................................................J.Liang,G.K.Venayagamoorthy,and R.G.Harley59 Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services...................E.Sortomme and M.A.El-Sharkawi70 Residential Appliances Identiﬁcation and Monitoring by a Nonintrusive Method..................Z.Wang and G.Zheng80(Contents Continued on page1)(Contents Continued from Front Cover)Modes of Operation and System-Level Control of Single-Phase Bidirectional PWM Converter for Microgrid Systems.. ...................................D.Dong,T.Thacker,I.Cvetkovic,R.Burgos,D.Boroyevich,F.F.Wang,and G.Skutt93 Generation-Load Mismatch Detection and Analysis...............................................R.M.Gardner and Y.Liu105 A Fault Location Technique for Two-Terminal Multisection Compound Transmission Lines Using Synchronized Phasor Measurements................................................................C.-W.Liu,T.-C.Lin,C.-S.Yu,and J.-Z.Yang113 Modeling and Control System Design of a Grid Connected VSC Considering the Effect of the Interface Transformer Type.................................................................................................H.Mahmood and J.Jiang122 Proﬁle of Charging Load on the Grid Due to Plug-in Vehicles................S.Shahidinejad,S.Filizadeh,and E.Bibeau135 Sizing of Energy Storage for Microgrids...........................................S.X.Chen,H.B.Gooi,and M.Q.Wang142 On the Accuracy Versus Transparency Trade-Off of Data-Mining Models for Fast-Response PMU-Based Catastrophe Predictors.........................................................................I.Kamwa,S.R.Samantaray,and G.Joós152 Optimal Power Allocation Under Communication Network Externalities..................................................... ..........................................................................M.G.Kallitsis,G.Michailidis,and M.Devetsikiotis162 Optimal PMU Placement by an Equivalent Linear Formulation for Exhaustive Search...................................... ......................................................S.Azizi,A.S.Dobakhshari,S.A.Nezam Sarmadi,and A.M.Ranjbar174 Towards Optimal Electric Demand Management for Internet Data Centers................J.Li,Z.Li,K.Ren,and X.Liu183 High Level Event Ontology for Multiarea Power System....................Y.Pradeep,S.A.Khaparde,and R.K.Joshi193 Linear Active Stabilization of Converter-Dominated DC Microgrids..........A.A.A.Radwan and Y.A.-R.I.Mohamed203 Analysis and Methodology to Segregate Residential Electricity Consumption in Different Taxonomies................... ...............................................................................J.D.Hobby,A.Shoshitaishvili,and G.H.Tucci217 Quality of Optical Channels in Wireless SCADA for Offshore Wind Farms..........................................X.Liu225 Calculating Frequency at Loads in Simulations of Electro-Mechanical Transients........J.Nutaro and V.Protopopescu233 Smart“Stick-on”Sensors for the Smart Grid........................................R.Moghe,mbert,and D.Divan241 The Load as an Energy Asset in a Distributed DC SmartGrid Architecture....R.S.Balog,W.W.Weaver,and P.T.Krein253 A Network Decoupling Transform for Phasor Data Based V oltage Stability Analysis and Monitoring..................... .............................................................................W.Xu,I.R.Pordanjani,Y.Wang,and E.Vaahedi261 A Two Ways Communication-Based Distributed Control for V oltage Regulation in Smart Distribution Feeders.......... ........................................................................H.E.Z.Farag,E.F.El-Saadany,and R.Seethapathy271 Investigation of Domestic Load Control to Provide Primary Frequency Response Using Smart Meters.................... .................................................................................K.Samarakoon,J.Ekanayake,and N.Jenkins282 SPECIAL SECTION ON TRANSPORTATION ELECTRIFICATION AND VEHICLE-TO-GRID APPLICATIONS GUEST EDITORIALSpecial Section on Transportation Electriﬁcation and Vehicle-to-Grid Applications................................A.Emadi295SPECIAL SECTION PAPERSA Novel Integrated Magnetic Structure Based DC/DC Converter for Hybrid Battery/Ultracapacitor Energy Storage Systems.............................................................................................O.C.Onar and A.Khaligh296 Performance Evaluation of an EDA-Based Large-Scale Plug-In Hybrid Electric Vehicle Charging Algorithm............ ............................................................................................................W.Su and M.-Y.Chow308 Source-to-Wheel(STW)Analysis of Plug-in Hybrid Electric Vehicles....S.G.Wirasingha,R.Gremban,and A.Emadi316 Prototype Design and Controller Implementation for a Battery-Ultracapacitor Hybrid Electric Vehicle Energy Storage System..........................................................................................Z.Amjadi and S.S.Williamson332 PEV Charging Proﬁle Prediction and Analysis Based on Vehicle Usage Data................................................ ......................................................................A.Ashtari,E.Bibeau,S.Shahidinejad,and T.Molinski341 Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services..............E.Sortomme and M.A.El-Sharkawi351 Online Estimation of State of Charge in Li-Ion Batteries Using Impulse Response Concept................................ .......................................................................A.H.Ranjbar,A.Banaei,A.Khoobroo,and B.Fahimi360 Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles........D.Wu,D.C.Aliprantis,and L.Ying368 Catenary V oltage Support:Adopting Modern Locomotives With Active Line-Side Converters............................. ........................................................................................B.Bahrani,A.Rufer,and M.Aeberhard377 An Optimized EV Charging Model Considering TOU Price and SOC Curve................................................. ..................................................................Y.Cao,S.Tang,C.Li,P.Zhang,Y.Tan,Z.Zhang,and J.Li388(Contents Continued on page2)(Contents Continued from page1)Spatial and Temporal Model of Electric Vehicle Charging Demand...............................S.Bae and A.Kwasinski394 Study of PEV Charging on Residential Distribution Transformer Life......................................................... ......................................................................Q.Gong,S.Midlam-Mohler,V.Marano,and G.Rizzoni404 Evaluation and Efﬁciency Comparison of Front End AC-DC Plug-in Hybrid Charger Topologies.......................... .......................................................................F.Musavi,M.Edington,W.Eberle,and W.G.Dunford413 Design of a Novel Wavelet Based Transient Detection Unit for In-Vehicle Fault Determination and Hybrid Energy Storage Utilization.........................................................C.Sen,ama,T.Carciumaru,X.Lu,and N.C.Kar422 Vehicle-to-Aggregator Interaction Game..........................................C.Wu,H.Mohsenian-Rad,and J.Huang434 Optimized Bidding of a EV Aggregation Agent in the Electricity Market..................................................... .....................................................................R.J.Bessa,M.A.Matos,F.J.Soares,and J.A.P.Lopes443 Coordinating Vehicle-to-Grid Services With Energy Trading..............................A.T.Al-Awami and E.Sortomme453 Energy Management Optimization in a Battery/Supercapacitor Hybrid Energy Storage System............................ ...........................................................................................M.-E.Choi,S.-W.Kim,and S.-W.Seo463 BEVs/PHEVs as Dispersed Energy Storage for V2B Uses in the Smart Grid......C.Pang,P.Dutta,and M.Kezunovic473 An Evaluation of State-of-Charge Limitations and Actuation Signal Energy Content on Plug-in Hybrid Electric Vehicle, Vehicle-to-Grid Reliability,and Economics...................................C.Quinn,D.Zimmerle,and T.H.Bradley483 Modeling of Plug-in Hybrid Electric Vehicle Charging Demand in Probabilistic Power Flow Calculations................ ............................................................................................................G.Li and X.-P.Zhang492 The Evolution of Plug-In Electric Vehicle-Grid Interactions......................................D.P.Tuttle and R.Baldick500 Methodology to Analyze the Economic Effects of Electric Cars as Energy Storages......................................... ssila,J.Haakana,V.Tikka,and J.Partanen506 An Economic Analysis of Used Electric Vehicle Batteries Integrated Into Commercial Building Microgrids............. .............................................S.Beer,T.Gómez,D.Dallinger,I.Momber,C.Marnay,M.Stadler,and i517 Transport-Based Load Modeling and Sliding Mode Control of Plug-In Electric Vehicles for Robust Renewable Power Tracking............................................................................................S.Bashash and H.K.Fathy526 Intelligent Energy Resource Management Considering Vehicle-to-Grid:A Simulated Annealing Approach.............. ..........................................................................T.Sousa,H.Morais,Z.Vale,P.Faria,and J.Soares535 Grid Integration of Electric Vehicles and Demand Response With Customer Choice........................................ ...............................................................................S.Shao,M.Pipattanasomporn,and S.Rahman543 Analysis of the Filters Installed in the Interconnection Points Between Different Railway Supply Systems............... ......................................................................................................M.Brenna and F.Foiadelli551 Autonomous Distributed V2G(Vehicle-to-Grid)Satisfying Scheduled Charging............................................. ............................................Y.Ota,H.Taniguchi,T.Nakajima,K.M.Liyanage,J.Baba,and A.Yokoyama559 Implementation of Vehicle to Grid Infrastructure Using Fuzzy Logic Controller.........M.Singh,P.Kumar,and I.Kar565。

Linux操作系统修改内核参数的三种方法详细说明

Linux操作系统修改内核参数的三种方法详细说明linux内核的参数设置怎么弄呢，Linux 操作系统修改内核参数有以下三种方式：修改 /etc/sysctl.conf 文件;在文件中加入配置项，格式为 key = value，保存修改后的文件，执行命令 sysctl -p 加载新配置。

使用 sysctl 命令临时修改;如：sysctl -w net.ipv4.tcp_mem = “379008 505344 758016”直接修改/proc/sys/ 目录中的文件。

如：echo “379008 505344 758016” 》 /proc/sys/net/ipv4/tcp_mem 注意：第一种方式在重启操作系统后自动永久生效;第二种和第三种方式在重启后失效。

内核参数kernel.core_uses_pi d = 1core_uses_pid 可以控制 core 文件的文件名中是否添加 pid 作为扩展名。

设置为1，表示添加 pid 作为扩展名，生成的 core 文件格式为core.xxx;设置为0（默认），表示生成的 core 文件统一命名为 core。

kernel.core_pat te rn = corecore_pattern 可以控制 core 文件的保存位置和文件格式。

如：kernel.core_pattern = “/corefile/core-%e-%p-%t”，表示将core 文件统一生成到 /corefile 目录下，产生的文件名为 core-命令名-pid-时间戳。

以下是参数列表：%p - insert pid into filename 添加 pid%u - insert current uid into filename 添加当前 uid%g - insert current gid into filename 添加当前 gid%s - insert signal that caused the coredump into the filename 添加导致产生 core 的信号%t - insert UNIX ti me that the coredump occurred into filename 添加 core 文件生成时的 unix 时间%h - insert hostname where the coredump happened into filename 添加主机名%e - insert coredumping executable name into filename 添加命令名kernel.msgmax = 8192进程间的消息传递是在内核的内存中进行的。

毕业设计(论文)基于智能pid的直流电机调速系统

摘要由于变频技术的出现，交流调速一直冲击直流调速，但综观全局，尤其是我国在此领域的现状，再加上全数字直流调速系统的出现，提高了直流调速系统的精度及可靠性，直流调速仍将处于重要地位。

对于直流调速系统转速控制的要求有稳速、调速、加速或减速三个方面，而在工业生产中对于后两个要求已能很好地实现，但工程应用中稳速指标却往往不能达到预期的效果，稳速要求即以一定的精度在所需要的转速上稳定运行，在各种干扰下不允许有过大的转速波动。

稳速很难达到要求原因在于数字直流调速装置中的PID调节器对被控对象及其负载参数变化自适应能力差。

模糊控制不要求被控对象的精确模型且适应性强，为了克服常规数字直流调速装置的缺点，本文将模糊控制与PID调节器结合，着手fuzzy-PID复合控制方案理论研究和硬件的实现，设计出相关控制方案的直流调速系统，该方案以AT89C51单片机为主控单元，合适的驱动电路和一些外围电路构成硬件系统；以参数模糊自整定PID为控制策略。

本文对于系统的硬件及软件设计进行了详细的设计，包括电机控制模块、检测模块、电机驱动模块的设计等，以及软件的控制思想和编程方法。

本系统的设计顺应了目前国外直流调速朝着数字化，发展的趋势，充分利用了单片机的优点，使得通用性得到了提高。

经过理论分析和设计此控制器的各项性能指标优于模糊控制器和常规PID 控制器，具有很强的鲁棒性。

关键词：模糊控制；直流调速；稳态性能；单片机AbstractAfter Frequency Conversion Technology appeared，AC speed regulation method had always impacted DC Speed Regulation，but Generally speaking，especially the status in our country，in addition to digital DC Speed Regulation emerged，it improving the precision and the reliability in DC Speed Regulation System．DC Speed Regulation was also in the important status．Speed stability、speed ratio、acceleration、deceleration are the four factors in DC Speed Regulation System，the last two factors already reached well in industry application．But the Stability index does not match the desired purpose．Stability index is that the DC motor running in the precision range on desired speed，even if the system has uncertain disturbance．It is hard to realize because of adaptiveability digital DC Speed Regulation device is not enough when in the condition of the load parameters change unpredictably．Fuzzy control does not need precision mathematic model to conquer the shortcoming in routine digital DC Speed Regulation．We can combine with the PID adjuster and fuzzy control，focusing on theory research and realization of fuzzy-PID compound control scheme，design relevant DC Speed Regulation System was designed in the dissertation．This scheme is based on the core of AT89C51 single chip，appropriate driver circuit and some peripheral circuits，Fuzzy Self-tuning PID is the control strategy,This dissertation also introduce the plan of hardware and software，including DC motor control module、driver module、examine circuit and so on in detail，if explained the method of control and the thought of software，this system got used to the trend of digital power in the international，used the single micro—computer fully，and improveed the general use of the power．Theoretical analysis and design showed that all performance indexes of Parameter Self-Adjusting Fuzzy Logic PID Controller was in advance of those of the simple fuzzy controller and the conventional PID controller．Especially，the adaptive fuzzy controller is robust．Keywords：fuzzy logic control(FLC)；DC Speed Regulation；stability performance；Single micro-computer目录摘要 .................................................................................................................................................I Abstract ......................................................................................................................................... II 目录 ............................................................................................................................................ I II第一章绪论 (1)1.1 序言 (1)1.2 PID 控制中存在的问题 (1)1.3 模糊控制的发展状况 (2)模糊控制的发展过程 (2)模糊控制技术要解决的问题 (3)1.4 直流调速系统的发展概况 (4)1.5 本课题的研究内容及目的 (5)第二章直流调速系统的理论分析 (6)2.1 控制理论在调速系统中的应用分析 (6)调速系统性能指标 (6)直流调速常用的方法 (7)2.2 传统直流调速系统中调节器参数的计算 (9)设计指标及要求 (9)固有、预置参数计算 (9)电流调节器参数计算 (10)转速调节器参数 (10)2.3 数字PID 调节器的原理及应用 (12)2.4 数字PID 控制器的算法实现 (14)第三章模糊PID 控制算法设计 (16)3.1 模糊控制的原理 (16)模糊控制的理论基础 (16)模糊控制系统的组成 (16)模糊控制在实际中的适用性 (17)3.1.4 模糊控制器的设计方法 (17)3.2直流调速系统模糊PID 控制结构设计 .......................................................................... 18 被控过程对参数P K 、I K 、D K 的自整定要求 (19)3.3模糊自整定PID 参数控器设计 (20)确定控制器的输入、输出语言变量 (20)3.3.2确定各语言变量论域，在其论域上定义模糊量 .............................................. 21 确定P K 、I K 、D K 的调节规则 .. (21)模糊推理和模糊运算 (22)第四章调速系统硬件设计 (24)4.1硬件总体方案设计 (24)4.2 主电路设计 (24)4.3 整流电力二极管参数的确定 (25)4.4 IGBT 的选择 (26)4.5 IGBT 驱动电路的设计 (26)IGBT 驱动电路的一般要求 (26)IGBT 的专用驱动集成电路 (26)4.6 泵升电压的抑制 (28)4.7 电流反馈信号检测装置设计 (29)概述 (29)4.7.2 电流检测装置的设计 (30)4.8转速检测环节及其与单片机接口电路的设计 (30)4.9 模拟量给定电流、转速反馈量与单片机的接口设计 (32)4.10 键盘与显示接口电路 (32)第五章系统软件设计 (34)5.1主程序 (34)5.2 A/D转换设计 (35)5.3键盘与显示子程序设计 (36)5.4模糊PID控制流程设计 (37)结论 (38)参考文献 (39)致谢 (42)第一章绪论1.1 序言在现代化的工业生产过程中，几乎无处不使用电力传动装置，生产工艺、产品质量的要求不断提高和产量的增长，使得越来越多的生产机械要求能实现自动调速。

数学建模灰色关联度分析英文版

4.1 Grey Relatio nal An alysisFirst,select a refere nee seque nee as show n below :x o k |k =1,2, n ；=[X o 1 ,x o 2 ,x°nAnd the other group of seque nee is,X i」.X j k |k=1,2, n=x 1 ,X i 2 ,x n ,i =1,2 ,mThen the correlation degree of x i to x0is,r iIn which,m[n min x0(t )—x s(t Pmaxmax x(t )—x s(t ji化) |x°(t )—Xs(t Pmaxmax|x°(t)—Xs(t jThen,we use r i to describe the correlation degree between x i and X ,namely to describe thein flue nee on X D caused by the cha nge of x i.In gen eral,Practical problems ofte n have differe nt nu mbers of differe nt dime nsion, but whe n we calculate the correlati on degree, it requires the same nu mbers of same dime nsion.So we want to carry out a variety of data processing dimensionless.in addition ,For comparison easily, all the sequseces are required to have a com mon poin t.I n order to solve these two problems, we transform the given sequences.The given sequenee x = x 1 ,x 2「，x n ] i, we nameI x(1) x(1) x(1)丿as initialization sequenee of Original sequenee x= x 1 ,x 2 ,x n「i4.2 Water resources carrying capacity evaluation indexes and classification indexesThe establishme nt of evaluati on in dex system of water resources carry ing capacity is a key issue in the study of water resources carry ing capacity. Regi onal water resources carry ing capacity is in flue need by many factors, Should be selected accord ing to the requireme nts of the specific regional social development backlog of social - economic index system response - naturalcompound ecosystem development scale and quality, reference existing literature results, through the grey correlatio n an alysis, the beari ng capacity of water resources evaluatio n in dex are show n in table 1. Approach A : Weighted Average Evaluati on Based ModelFig.1.The in dex system of water resources carry ing capacityComprehe nsive an alysis of water resources carry ing capacity of main in flue ncing factors and performa nee system, with reference to the evaluati on in dex system of water resources carry ing capacity at home and abroad to select the following 6 main factors as evaluation factors, The mea ning of the factors as follows:Water resource utilization ratio( u1) : water and the ratio of the total population( m /人);water utilization rate (u2) :The ratio of the available water quantity and water resources(%);utilizati on rate of arable land ( u3):Irrigation area and more on the ratio of the area(%);The modulus of water supply(4 3 2 u4) : water and the ratio of the land area (10 m /km);modulus of water requireme nt u5):The total water dema nd and the ratio of the land area(104m3 / km2)Life water use ratio ( u) : Domestic water amount and the ratio of the total water dema nd (%) Accord ing to the above six evaluati on factors on the in flue nee degree of the regi onal water resources carry ing capacity Will the in flue nee of these factors are divided into three levels, Eachfactor, the number of each level indicators are shown in table 2. The said condition is bad, water resources carrying capacity is close to saturation value, further development and utilization of potential is smaller, develop water shortages will occur;Membership condition is good, said there are still large carry ing capacity of water resources in this area, water resource utilizati on degree of developme nt in this area are small, water supply situati on more optimistic;Level is betwee n the above two levels, which in dicates that this area has a scale developme nt for the supply of water resources development, but still has a certain ability of development and utilization of local econo mic developme nt and people have certa in guara ntee survival.The evaluation factors V 1 V2 V3>2250 2250--1000 <1000m3人-1Per capita water supply / ‘Water resources utilization % <30 30--60 >60 The modulus of water supply / <80 80--100 >1004 3 2 110 m (km )Farmland irrigation rate /% <20 20--50 >50 Water dema nd modulus / <60 60--100 >1004 3 2 110 m (km )Domestic water rate /% >4 4--2 <20.95 0.5 0.05 ScoresTable 2: classification index comprehensive evaluation factors4.3 The fuzzy comprehe nsive evaluati on modelBecause of the beari ng with un certa in ty a nd fuzz in ess, the fuzzy evaluati on model can be well carried out on the water resources carry ing capacity multi-factor and multi-level comprehe nsive evaluati on, comprehe nsive to reflect the status of the regi onal water resources carry ing capacity. Fuzzy evaluati on forB=ARfinite field ' - - ，'- :, With U on behalf of the comprehensiveevaluati on factors of collect ion, V on behalf of the evaluatio n of collect ion. Type A is U in the fuzzy subsetCEi 口壬...tt-f.M A,冬1, Q；A={ - - } ,0一一for U for A membership .It marked the sin gle factor evaluatio nfactors in the role of size, To a certa in exte nt, also on behalf of the rat in gs; The evaluati on result B■ 1 bj m j hf. £iff鲨1is the fuzzy subset of V .B={ 一一}, 0 , for the level of comprehe nsive evaluati on the membership degree of fuzzy subset B, they say the result of comprehe nsive evaluati on.Evaluation matrix«T III0.5(1 0.5(1 4) 0.5(1 卅)0.5(1 k3 ~ui)k3 -u iu i :: k1K 乞u i ::: k2K 空u i ::: k3u i - k3Among r ij for U i evaluation of grade R =(斤石2…r in) membership degree, Matrix of the ith line is the single factor evaluation results of the ith a factor.Evaluation computer matrix A representative of the various factors on the comprehensive evaluati on of the importa nee of weight coefficie nt, thus satisfy, fuzzy tran sform AR also can be degraded for com mon matrix is calculated.4.3.1 The calculati on of matrix RCorresponds to the evaluation sets of evaluation factors, and can be through the evaluation factors in the evaluation matrix R the actual numerical control classification indexes of variousfactors to analysis and calculation.ln order to make the membership function can smooth tran siti on betwee n at all levels, the blur process ing. Of V2 point of membership degree is 0.5, the midpo int to accord ing to lin ear regressive process ing. For V1 and V3 on both sides of the in terval, then make the farther away from the critical value of range on either side of the membership degree, the greater the belong to both sides on the critical level of membership you 0.5 accord ing to the grades of the above ideas to con struct a evaluatio n the calculati on formula of subordi nate fun ctio n. V1 and V2 level threshold for K1, V2 and V3 threshold for K3, V2 mid-ra nge level in terval for K2.K2.For evaluating factors u3 u4 u5 u6 and comments and calculating formula for relative membership degree function:0.5(1 + kt一5 ) U i v k1k2 - U iu“ =三0.5(1 k^ u i::: k2k2-匕(1)皿二 0.5(1k 2 乞 5：：： k 3 (3)k 2 - k 3U i 兰 k 2Calculate the relative membership degree of evaluation factors according to the type, n, * «3^ M s For evaluation factors U1 in the evaluation and the calculation of the relative membership degree function simply type (1) ~ (3) the right end of type UI interval good "-"i nstead of' -"; And "<" in stead of ">" after the calculati on formula of the same4.3.2 Fuzzy comprehe nsive evaluati onAccord ing to the evaluati on factors in flue nee the size of the carry ing capacity of waterresourcesWill judge factors in flue nee on water resources carry ing capacity en dowed with differe nt weights Accord ing to the matrix A and R, B = AR accord ing to com mon matrix calculation rules can be obtained by matrix of ultimate bearing capacity of water resourcesevaluation results, according to the corresponding classification index score values ■"■ "■regional water resources carrying capacity comprehensive evaluation value can be obtained, Regi onal water resources carry ing capacity comprehe nsive evaluati on value can beobta ined The regi onal water resources carry ing capacity.The comprehensive evaluation and analysis. K value is to highlight the role of the dominant level, usually take 1 in arid areas.6' b jk ：' j6、b j k0.5(1k 2 —5 U i - k 3。

小型模块化反应堆控制方法综述

2024 年 3月第 61 卷第 2 期Mar. 2024Vol. 61 No. 2四川大学学报（自然科学版）Journal of Sichuan University （Natural Science Edition ）小型模块化反应堆控制方法综述张薇薇1，何正熙2，万雪松1，刘方圆3，邓科1，肖凯2，罗懋康1（1.四川大学数学学院，成都 610064； 2.中国核动力研究设计院核反应堆系统设计技术重点实验室，成都 610213； 3.中国核动力研究设计院反应堆燃料及材料重点实验室，成都 610213）摘要: 小型模块化核反应堆具有建造周期短、安全性高、运维成本低、适应性强、应用领域广等显著优势，广受世界各国关注，也是我国的战略性需求.发展具有自适应、强鲁棒、高可控和高可信特性的新型控制方法，有效降低甚至消除对控制人员值守的依赖，是小型模块化核反应堆的一个重要发展趋势.智能化、自动化的反应堆控制系统通过高效的控制动作来实时跟踪负荷需求，进而有效提高反应堆的稳定性、可靠性和安全性.本文对小型模块化核反应堆控制方法的研究现状进行了综述.本文首先回顾了基于经典控制理论的传统PID 控制方法的原理及其优缺点，然后总结了当前应用于反应堆控制系统的一些高精度、高效率智能控制方法，如模糊控制、神经网络控制、智能优化控制、复合控制方法等的主要特点.最后，针对当前小型模块化反应堆控制系统的应用需求和技术难点，本文对智能控制方法的可能发展方向进行了展望.关键词: 小型模块化反应堆；反应堆控制； PID 控制；智能控制；复合控制中图分类号: O29 文献标志码: A DOI : 10.19907/j.0490-6756.2024.020001A review on the control methods in small modular reactorsZHANG Wei -Wei 1， HE Zheng -Xi 2， WAN Xue -Song 1， LIU Fang -Yuan 3，DENG Ke 1， XIAO Kai 2， LUO Mao -Kang 1（1.School of Mathematics ， Sichuan University ， Chengdu 610064， China ； 2.Science and Technology on Reactor System Design Technology Laboratory ， Nuclear Power Institute of China ， Chengdu 610213， China ；3.Science and Technology on Reactor Fuel and Materials Laboratory ， Nuclear Power Institute of China ，Chengdu 610213， China ）收稿日期:2023-10-26基金项目: 基于机器学习的复杂系统模型机理数据融合技术研究(SCU ＆DRSI -LHCX -6)作者简介: 张薇薇（1995—），女，河南开封人，博士研究生，主要研究方向为人工智能.E -mail: lfyzwscu@ 通讯作者: 何正熙.E -mail: hezhengxi0002@特约综述何正熙，男，中共党员，研究员级高级工程师，长期从事核电站仪表与控制系统相关研究设计工作，申请专利百余件，公开发表论文30余篇，负责制定IEC 国际标准1项、国家标准1项、能源行业标准2项，获省部级二等奖3项、三等奖5项，荣获中核集团彭士禄核动力创新青年人才、中国核能行业协会青年优秀创新人物等荣誉称号.第 61 卷四川大学学报（自然科学版）第 2 期Abstract: Due to the significant advantages such as short construction cycle， high safety performance， low operation and maintainence costs and strong adaptability， the small modular reactors （SMRs） have long been a focus of researchers around the world.Nowadays，it has also become a strategic need of our country.Re‑cently， it has been clear that one of the promising development directions of intelligent control systems of the SMRs lies in the unmanned control systems therein some advanced control methods are applied with high ro‑bustness and reliability.These control systems can track load demand in real-time through efficient control ac‑tions and thus effectively improve the stability， reliability and safety of SMRs.Meanwhile， these systems can also reduce or even eliminate the dependence on operators significantly.In this paper， the mainstream control methods applied in SMRs are briefly reviewed.Firstly，principles and characteristics of the traditional PID control method based on the classical cybernetics are surveyed.Then， some intelligent control methods with higher accuracy and efficiency implemented in the reactor control systems，such as the fuzzy logic inference method， the neural network control method， the compound control method and the composite control method are summarized.Finally， facing to the requirements and technical problems of control systems in the SMRs，some potential research directions of intelligent control methods are prospected.Keywords: Small modular reactor； Reactor control； PID control； Intelligent control； Compound control1　引言随着经济发展和生活水平的不断提高，全球的能源需求持续增长.当前在全球范围内，能源的主要来源依然是煤、石油、天然气等化石能源.这些能源不但污染大，而且在短时间内不可再生［1］，无法满足人类长期可持续的能源需求.因此，发展可再生、安全且清洁的能源技术是解决能源危机的必然选择［2］，核能正是其中一种高效清洁能源［3］.历史上，核反应堆经历了先军用后民用的发展历程.民用反应堆一般通过提升反应堆的功率来降低成本、提高市场竞争力，这就导致核电厂逐渐大型化.另一方面，受到实际功率需求和使用空间的限制，军用核反应堆的功率水平一般远小于民用反应堆，更偏向小型化.相对于大型核反应堆，小型化反应堆普遍采用模块化和一体化设计，并采用非能动安全系统［4-6］，以便有效提高反应堆的安全性和经济性.小型模块化反应堆（Small Modular Reactor，SMR）具有功率密度低、体积小、建造周期短、安全性能高、运行维护成本较低、选址成本低、适应性强、部署灵活性高［7］等显著优势，因而在世界各国得到广泛应用［8-11］.当前，我国对不受环境影响、长寿命且安全可靠的无人化SMR的需求十分迫切.在国家发展改革委、国家能源局发布的《能源技术革命创新行动计划（2016—2030年）》［12， 13］中，明确提出我国将继续深入实施创新驱动发展战略，进一步完善核能领域科技研发体系，重点支持SMR的发展和研究.值得注意的是，美国、日本等国家从上世纪九十年代初［14］就已经对SMR及其应用开展了相当规模的研究，而我国在这方面的研究尚处于起步阶段.在确保安全的前提下，无人化SMR能够摆脱对操控人员的值守依赖，提升反应堆的控制效能，是小型模块化反应堆的重要发展趋势之一.SMR高可用性的关键是避免不必要的停堆和减少换料维修时间.这需要有一套具有足够容错性、鲁棒性的高可靠、自动化控制系统.这些控制系统的设计和运转各有其控制方法和策略，具有不同的效能和应用领域.传统的PID控制方法虽然操作简单灵活，静态特性好，且在工程中已有广泛应用［15］，但该方法仅适用于线性时不变系统的控制［16］.对于核反应堆等复杂非线性系统而言［5］，其本身具有较强的模型和参数不确定性，在运行过程中会受到大量外部干扰，因而传统PID控制方法无法很好地控制和处理这些强不确定因素.近年来，随着控制理论的发展［17］，国内外研究者为提高核反应堆控制系统的性能不断探索新的控制方法，逐渐发展出一些智能化的控制和优化方法，较好地解决反应堆控制系统中普遍存在的强耦合、多变量、长时延及非线性等关键控制问题.在此基础上，出现了一些复合控制方法，如神经网络PID控制、模糊神经网络控制等，进一步融第 2 期张薇薇，等：小型模块化反应堆控制方法综述第 61 卷合了多种智能控制方法.应用这些智能化控制方法，反应堆可以通过更高效的控制动作来实时跟踪负荷需求，显著提高控制效率和安全性能.在本文中，我们系统总结了当前应用于反应堆控制系统中的一些传统和智能化控制方法，分析了经典PID 控制方法以及智能控制方法的机制、优缺点及研究现状.最后，基于应用需求和问题难点，我们展望了SMR 控制方法的发展趋势和研究方向.2　PID 控制方法PID 控制方法不依赖于控制对象的精确数学模型，而是通过控制变量偏差的变化幅度、累积效果和趋势及控制变量之间的简单相互影响关系等使得控制变量的输出逐渐趋近预期的控制效果.PID 控制方法具有原理清晰易懂、易于工业实现、鲁棒性好等优点.PID 控制方法在核反应堆控制系统中已有普遍应用.汪等［18］采用PID 控制方法实现对钍基熔盐堆核能功率的控制.在合适的PID 参数集下，该方法可以实现控制系统的快速响应、良好系统鲁棒性和抗干扰能力.雍等［19］基于压水堆核电厂蒸汽发生器水位模型分别设计了单PID 控制器、串级PID 控制器及双PID 控制器，并分析了每种控制方案的优缺点.多数反应堆控制系统方案基于经典控制论的单输入单输出闭环串级PID 控制方法，其原理如图1所示.该方法主要考虑系统的外部特性，是对系统的不完全外部描述，适用于单输入单输出、线性、定常、集中参数的对象［16］.PID 控制方法的原理简单［16， 20］，且在反应堆长期运行过程中积累了相当多的参数调节经验，因而当前在工程控制领域具有主导地位.但是，传统的PID 控制方法缺乏自调节能力.这就使得该方法在面对复杂控制对象时的响应速度、超调量等指标难以实现进一步优化，因而在非线性系统中难以获得理想的控制效果.此外，常规的PID 控制系统不能自动地适应反应堆运行环境的复杂变化，在面对复杂工况时仍需要反应堆运行维护人员频繁进行手动操作，持续监督系统重要参数的变化，因而对操作人员的专业能力和心理素质要求较为苛刻，可能影响核动力装置的经济效益和安全可靠性.3　智能控制方法核反应堆系统极其复杂，通常无法用数学模型较好地进行概括和近似，从中提取出理想的控制模型.在这种情况下，神经网络、模糊控制等非解析方法可能具有较为明显的优势.3.1　神经网络控制方法不同于经典PID 控制方法，神经网络控制方法不依赖于数学模型，而是从对象的输入输出数据中学习得到仿真模型，避开人为提取被控对象或设计控制器解析模型这一难题.该方法利用智能方法的预测和优化能力将控制系统的设计问题转化为优化问题.由于其具有自学习、非线性、并行计算和强鲁棒性等特点，在控制领域内得到了广泛应用.肖等［21］针对反应堆堆芯具有非线性、时变性等特点，且经典控制方法难以实现全工况内反应堆功率的良好控制的情况，提出了一种反应堆功率的神经网络预测控制方法.他们以国际革新安全反应堆（IRIS ）为研究对象进行了仿真验证，结果表明该方法可以实现堆芯入口温度扰动和变负荷工况下反应堆功率的良好控制.张等［22］采用核电站的真实监测数据，分别优化了基于时间序列的LSTM 和基于特征再提取的CNN 模型，发现基于上述模型可以有效预测核反应堆堆芯热功率分布.Lu 等［23］以KLT -40S 核反应堆堆芯和蒸汽发生器作为研究对象，建立了基于深度学习的核反应堆系统热工参数预测方法，实现了对核反应堆系统热工参数的快速预测.Xiao 等［24］提出了一种小型压水堆的神经网络预测功率控制方法，以解决目前反应堆控制中采用的预测控制算法模型普遍存在识别精度较低的问题.小型压水堆的堆芯在典型瞬态工况下的仿真结果表明，该方法具有良好的负荷跟踪性能和较强的抗干扰能力.袁等［25］设计了一种神经网络监督控制系统，用于船用一体化压水堆功率的控制，其中的PID 控制器是反馈控制器，神经网络则是前馈控制器，其结构如图2所示.对压水堆功率控制的仿真结果表图1　单输入单输出PID 控制系统原理Fig.1　Block diagram of the single input and single outputPID control principle第 61 卷四川大学学报（自然科学版）第 2 期明，与传统的PID 控制相比，神经网络监督控制具有较强的鲁棒性和自适应能力，能有效地提高控制精度.经过文献调研，我们认为目前将神经网络控制方法应用于小型反应堆控制系统中主要有3种思路：（1）利用神经网络的自适应、自学习功能优化控制系统的参数；（2）建立描述控制对象输入输出的映射关系（模型），即建立输入与输出之间的神经网络模型；（3）与其他方法相结合形成复合神经网络控制［26］，如与进化算法结合实现反应堆功率控制，与鲁棒控制技术结合实现对蒸汽发生器水位的控制等.这种复合控制方法可以将神经网络和其他智能算法的优势结合起来，有望取得较好控制效果.总之，神经网络不依赖数学模型但可以不断逼近模型的函数，其核心是修改激励命令与对象状态之间的映射来提高控制效果，并对网络连接权重进行优化.相对于传统的PID 控制方法，该方法具有诸多优点，如神经网络具有并行机制、模式识别、记忆和自学习能力的特点，能够学习与适应不确定系统的动态特性，能够充分逼近任意复杂的非线性系统，有很强的鲁棒性和容错性，等.但同时该方法也存在参数选择和优化过程复杂、训练时间长、可解释性差、对数据质量的要求较高等不足.3.2　模糊控制方法模糊控制方法的基本思想是把人的操作经验当作控制模型，把模糊语言、模糊集及模糊推理作为数学工具，将准确测量结果模糊化，再经过模糊推理后准确化，进而实现智能控制.基于被控系统的物理特性，模糊控制能够模拟人的思维方式和控制经验，提供一种基于自然语言描述规则的控制规律的新机制.一般而言，凡是无法或难以建立数学模型的问题都可以通过模糊控制方法来解决［27-30］.模糊控制可以忽略对象的输入输出数据，从获取对象的“知识”这一角度出发来认识被控对象，甚至直接从专家和操作人员的知识和经验中形成“model -free ”控制器.模糊推理是模糊控制方法的核心，具有基于模糊概念的拟人化推理能力.该推理过程基于模糊逻辑中的蕴含关系及推理规则来进行［31］，其控制单元的基本功能结构如图3所示.模糊控制方法在反应堆控制系统中也有应用.Li 和Ruan ［32］比较了模糊控制、PID 控制及自适应模糊控制等控制方法在反应堆控制方面的效果，发现模糊控制与PID 控制相比具有较好的灵活性、鲁棒性，而且先进模糊控制可以动态调整规则库，具有更强的鲁棒性.Kim 等［33］设计了一种用于稳定蒸汽发生器水位的智能模糊控制器，获得了良好的控制效果.Rojas -Ramírez 等［34］提出一种控制反应堆功率调节至设定值的自适应模糊控制系统，通过建立李雅普诺夫函数来保证系统的稳定性，实现反应堆在安全范围内快速调节到设定功率的目的，减少了运行过程中的功率波动.原和黄［35］针对核蒸汽供应复杂系统的控制问题，提出了一种基于T -S 模糊控制器的控制系统.仿真结果表明，该方法比传统的线性PI 控制器具有更好的控制效果.贾等［36］在多用途重水研究堆上研究了功率调节系统的模糊控制，设计了Mamdani 型二维模糊功率控制器.仿真结果显示，其反应堆功率调节系统在采用该模糊控制器后是稳定的，并且负荷跟随特性良好，其控制性能优于经典PID 控制器.综上，在小型反应堆控制系统的应用中，相比PID 控制方法，模糊控制方法无需被控对象的精准数学模型，具有强鲁棒性，且处理过程模仿人的思维，更适用于解决小型反应堆控制过程中非线性、强耦合、时变滞后等方面的问题，并在一定程度上图2　压水堆功率控制系统原理［26］Fig.2　Block diagram of the PWR power control systemprinciple［26］图3　模糊控制单元的基本功能结构Fig.3　Basic functional structure of a fuzzy control unit第 2 期张薇薇，等：小型模块化反应堆控制方法综述第 61 卷抑制噪声.但是，由于信息的模糊处理容易导致系统的控制精度降低，并且该方法缺乏系统性，无法定义控制目标，因而该方法在小型反应堆的控制应用中需要与其他控制方法结合才能达到更好控制效果.3.3　专家系统控制方法1983年， Astrom ［37］首先将专家系统引入智能控制领域，并于1986年正式提出了专家控制的概念.专家系统可以处理定性、启发式的或不确定的知识信息，通过推理［38， 39］来实现任务目标.基于专家系统发展而来的专家控制方法具有许多领域专家的知识和经验，能够解决专门性问题.该控制方法改变了传统控制方法依赖数学模型的方式，实现了知识模型与数学模型、知识处理技术与控制技术的结合［40， 41］，有利于解决复杂非线性系统的控制难题.按照作用机理，我们可将专家控制系统的结构类型分为直接型专家控制和间接型专家控制两种［42］.直接型专家控制系统直接控制生产过程与被控对象，其原理如图4所示.该控制器的任务和功能相对简单，专家系统直接被包含在控制回路中，直接给出控制信号来控制被控过程.在每一个采样时刻，控制系统均需要专家系统根据知识库规则和测量过程信息推导给出控制信号，因而该类控制系统对推理速度的要求较高.间接型专家控制是常规PID 控制器、自适应控制和专家系统的结合，其控制原理如图5所示.该方法的作用方式是根据系统运行情况调整控制器参数，选择合适的控制方法［41］，以实现优化适应、协调、组织等高层决策的智能控制.间接型控制器可以实现优化、适应、协调、组织高层决策.目前，专家控制与其他控制方法的结合在反应堆控制中更为普遍.陈等［43］针对核电厂系统的故障特征建立了一个专家系统，通过引入Rough 集理论来解决专家系统中的知识获取问题.该方法可以准确诊断系统中的故障问题.彭和余［44］为解决识别核动力装置的故障问题，采用面向对象的模糊Petri 网知识表示方法对专家系统的知识库进行改进.这种改进的专家系统可以准确地识别系统故障.Liao 等［45］开发了一种反应堆冷态功能试验智能专家系统，改变了依靠人工读取、传输和处理数据的传统低信息化测试方法，该系统具有试验过程控制、实时数据采集与结果分析和数据存储等功能.综上，专家控制方法是在控制闭环中加入经验丰富的控制专家，控制系统作为工具可以自行选择各种方法，本质上是对“控制专家”的思路、经验、策略的模拟、延伸、扩展，具有透明度高、灵活性强、知识信息处理系统强等优点.但该方法需要获得专家知识，因而建造通用专家开发工具，并且稳定性和可控性理论分析较难.4　智能优化方法近年来，随着优化理论的不断发展，除了前面提到的模糊控制、神经网络控制等方法之外，还有许多智能优化算法被用于解决反应堆控制系统中的参数优化问题.这些算法主要包括粒子群算法、遗传算法、禁忌搜索算法等.遗传算法（Genetic Algorithm ， GA ）是由密歇根大学的Holland 教授于1962年首次提出的，其基本思想是模拟生物进化中优胜劣汰、适者生存的法则，根据适应度函数衡量解的品质并通过复制、交叉等动作筛选个体，提高群体的适应度，进而迭代得到当前最优，最终得到全局最优［39， 46］.该算法适用于解决非线性、非凸、多峰等复杂函数的优化问题［47， 48］.应用遗传算法，Panda 和Padhy ［49］对核反应堆的电力系统稳定器和输电系统控制器进行了协调控制，给出了各扰动条件下电力系统的非线性仿图4　直接型专家控制系统原理Fig.4　Block diagram of the direct expert control systemprinciple图5　间接型专家控制系统原理Fig.5　Block diagram of the indirect expert control systemprinciple第 61 卷四川大学学报（自然科学版）第 2 期真结果，验证了该方法的有效性.刘等［50］设计了一种反应堆平均温度线性自抗扰控制器，采用遗传算法优化控制器参数，解决了自抗扰控制器参数不易整定的问题.仿真结果表明，该优化方法对控制器参数进行优化是有效的，且具有良好的鲁棒性.Wan和Zhao［51］采用带精英策略的非支配排序遗传算法，对AP1000反应堆轴向功率分步控制系统中冷却剂平均温度（Tavg）通道的超前/滞后时间常数和功率偏差通道的非线性增益进行了多目标优化，以阶跃瞬态时反应堆功率的超调量和Tavg超调量作为最小为优化目标.结果表明，优化后的反应堆功率和Tavg控制效果能够得到明显改善.粒子群优化算法（Partical Swarm Optimiza‑tion， PSO）是Eberhart和Kennedy受到鸟群觅食行为的启发于1995年提出的一种基于群体协作的随机搜索算法［52］.该算法通过个体之间的协同合作寻找适应度最小的最优解.同遗传算法相比，该算法需要调整的参数更少，更易实现.目前，粒子群算法已被广泛应用于反应堆控制系统中函数优化、神经网络训练、模糊系统控制等方面［53］.Wang等［54］采用惯性权重线性递减的粒子群优化算法对AP1000反应堆轴向功率分布控制系统进行了参数优化，优化过程以Tavg控制回路中的超前/滞后时间常数和磁滞回环区间域的上、下限为优化变量，以减小核功率偏差和M棒组的移动步数为目标构建目标函数，同时在目标函数中增加罚函数，以保证在优化过程中所选取的优化变量满足约束条件，并使AO棒组始终在其目标控制带之内.结果表明，优化后的反应堆功率和轴向功率偏差在瞬态过程中的超调量减少、响应速度加快.5　复合控制方法复合控制方法是近年来控制论研究领域的热点之一，它融合了多种智能控制方法，将模糊推理、神经网络、PID控制、智能优化等控制方法交叉融合，以进一步提高控制系统的性能.目前，该方法在实验验证中已经取得了良好的控制效果. 5.1　智能PID控制方法随着控制论、计算机技术相关理论和方法的发展，在传统PID控制方法的基础上，部分研究者将PID控制方法与其他智能控制或优化方法相结合，提出了多种新的PID控制方法.其中比较典型的有神经网络PID控制、模糊PID控制方法以及基于智能优化的PID控制方法，等.5.1.1　神经网络PID控制方法　在小型反应堆控制系统中，PID控制是最常用且不依赖模型的控制方法，其控制效果依赖于比例、积分和微分系数的选取是否准确.但是，反应堆系统的复杂性、模型的不确定性使得比例、积分和微分增益的选取较为困难，进而影响到控制效果.神经网络与PID 控制器相结合的控制方法可以很好地抑制PID控制器所产生的超调问题，提高控制系统的稳定性、可靠性和灵活性.神经网络和PID控制方法相结合主要有以下几种方式：（1）将神经网络作为优化工具在线调整PID控制控制系统的参数；（2）将神经网络与PID控制器连接，通过优化神经网络的连接权值来调整PID控制器的参数；（3）神经网络作为控制器，将PID控制方法融合到神经网络结构中；（4）PID神经网络多变量解耦控制，等.Kong等［55］提出了一种基于径向基函数的神经网络蒸汽发生器液位PID控制策略，通过RBF神经网络对蒸汽发生器的数学模型进行辨识，然后根据过程的特征变化对PID参数进行调整.仿真结果表明，该方法能够根据过程的动态特性自适应优化PID控制器的参数，表明这个控制策略是有效的.肖等［56］为了实现PID控制器参数的在线调节，利用BP神经网络的自适应能力对PID参数进行实时整定，建立了堆芯功率BP神经网络PID 控制系统.仿真结果表明，BP神经网络PID控制方法与传统的PID控制方法相比具有超调量小、响应速度快等优点，控制效果好.Ding［57］提出了一种基于模糊神经网络模型的PID神经网络控制方法，采用模糊神经网络模型和梯度下降法在线调整PID神经网络权值，并将该方法应用于循环流化床锅炉床层温度控制.Govin‑dan和Pappa［58］设计了一种基于反馈线性化在线学习的神经网络自适应控制器，采用基于改进增量规则和投影算法的在线权值调整算法在线调整神经网络卡和PID控制器的参数，以解决高阶点动态压水堆（PWR）在局部、全局负荷跟随和应急工况下功率水平跟踪问题.该方法具有更快的响应速度、较好的自适应性和较小的稳态误差.Liu和Xia［59］针对PID控制器无法对复杂系统进行有效控制的问题，设计了一种基于有监督。

智能PID控制方法综述

于处理两大类问题【．７１：①难以用数学模型进行准确描述的大规模和复杂非线性系统，需要引入人为因素才能进行有效控制；②控制目标通常需要分解成多个子任务的系统。智能ＰＩＤ控制器吸收了智能控制与常规ＰＤ控制两者的优点【８】。首先，它具备自学习、自适应、自组织的能力，能
够自动辨识被控过程参数、自动整定控制参数，能够适应被控过程参数的变化；其次，它又具有
【参萼绥融｝＿母一ｒｔ。拄制器卜ｆ
ＧＡ搏法进化控制器
一被控对掾ｌ
图２神经网络ＰＩＤ控制器
图３基于遗传算法的ＰＩＤ控制器
基于遗传算法的自适应ｐＩＤ控制的一种原理框图如图３所示，图中省略了遗传算法的具体
操作过程。其思想就是将控制器参数构成基因型，将性能指标构成相应的适应度，便可利用遗传
算法来整定控制器的最佳参数，并且不要求系统是否为连续可微的，能否以显式表示。当遗传算
ＶａＩｕｅｏｆｉｎｄｕｓ仃ｉａｌｃｏｎ仃ｏｌ
Ａｍｅｒｉｃ卸．１９９５．
向算法和反传算法）进行离线学习，实时调整出ＰＩＤ参数，同时还要继续学习不断地调整神经网络中各神经元间权系数，以适应被控对象的变化，因此具有很强的适应性。
图２所示的是一种神经网络ＰＤ控制删１４】。其中的神经网络控制器部分实际是一个前馈控
制器，它建立的是被控对象的逆向模型。易看出，神经网络控制器通过向传统控制器的输出进行学习，在线调整自己，目标是使反馈误差ｅ或∥。趋近于零，从而使自己逐渐在控制作用中占据主导地位，以便最终取消反馈控制器的作用。但是以ＰＩＤ构成的反馈控制器一直存在，一旦系统出现干扰动，反馈控制器马上可以重新起作用。因此，采用这种前馈加反馈的智能控制方法，不仅可确保控制系统的稳定性和鲁棒性，而且可有效地提高系统的精度和自适应能力。文献【１５］提出了一种基于对角回归神经网络的ＰＩＤ控制器结构，分别建立了基于对角回归神经网络和ＢＰ网络的液位实时控制系统，具有较好的鲁棒性。

SIMATIC Energy Manager PRO V7.2 - Operation Operat

Disclaimer of Liability We have reviewed the contents of this publication to ensure consistency with the hardware and software described. Since variance cannot be precluded entirely, we cannot guarantee full consistency. However, the information in this publication is reviewed regularly and any necessary corrections are included in subsequent editions.
2 Energy Manager PRO Client................................................................................................................. 19
2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.5.1 2.1.5.2 2.1.6
Basics ................................................................................................................................ 19 Start Energy Manager ........................................................................................................ 19 Client as navigation tool..................................................................................................... 23 Basic configuration ............................................................................................................ 25 Search for object................................................................................................................ 31 Quicklinks.......................................................................................................................... 33 Create Quicklinks ............................................................................................................... 33 Editing Quicklinks .............................................................................................................. 35 Help .................................................................................................................................. 38

复合电源电动汽车能量管理策略研究

14.60 kWh/100 km,实现了节能效果&
4.3仿真结果对比
糊逻辑
，与单一电源纯电动汽
车，添加了超级电容的复合电源系统可以提高
动力电池的充放电效率，减少动力电池的放电循环
次数和放电电流。在2 400 s的NEDP 工况
，复电源系统的超级电容SEC值从0. 67 丁
0. 57,动力电池SEC值变化范围为0. 61〜
表1动力电池匹配参数
参数类型
参数取值
容量/ Ah SOC范围/%
144 15 〜95
额定电压/V 最大电压/V 串联数/个并联数/个电池总节数/个
372.3 428.4
117 2 234
驱动电机匹配参数见表3&
表3驱动电机匹配参数
参类型功率（峰值/额定）/kW 转速（峰值/额定）/（r - min-1 ）
2021年6月第50卷第6期
机械设计与制造工程 Machine Design and Manufacturing Engineering
?Jun.2021 Vol. 50 No. 6
DO【：10. 3969/j. issn. 2095 - 509X. 2021.06. 014
复合电源电动汽车能量管理策略研究
扭矩/(N・m) 额电 *V
参值 90 *45
11 000 *3 500
280 372.3
2.2复合电源电动汽车仿真模型使用AVL - Cruise软件建立的复合电源电动
汽车整车模型如图3所示。
3复合电源电动汽车能量管理策略
于复合电源系统的结构及其工作模式
糊逻辑
，根据不同的功率需求合理
动力电池和超级电容之间的功率输例,提

机器视觉英文词汇

机器视觉英文词汇机器视觉英文词汇Aaberration 像差accessory shoes 附件插座、热靴accessory 附件achromatic 消色差的active 主动的、有源的acutance 锐度acute-matte 磨砂毛玻璃adapter 适配器advance system 输片系统ae lock(ael) 自动曝光锁定af illuminatoraf 照明器af spotbeam projectoraf 照明器af(auto focus) 自动聚焦algebraic operation 代数运算一种图像处理运算，包括两幅图像对应像素的和、差、积、商。

aliasing 走样（混叠）当图像象素间距和图像细节相比太大时产生的一种人工痕迹。

alkaline 碱性ambient light 环境光amplification factor 放大倍率analog input/output boards 模拟输入输出板卡analog-to-digital converters 模数转换器ancillary devices 辅助产品angle finder 弯角取景器angle of view 视角anti-red-eye 防红眼aperture priority(ap) 光圈优先aperture 光圈apo(apochromat) 复消色差application-development software 应用开发软件application-specific software 应用软件apz(advanced program zoom) 高级程序变焦arc 弧图的一部分；表示一曲线一段的相连的像素集合。

area ccd solid-state sensors 区域ccd 固体传感器area cmos sensors 区域cmos传感器area-array cameras 面阵相机arrays 阵列asa(american standards association) 美国标准协会asics 专用集成电路astigmatism 像散attached coprocessrs 附加协处理器auto bracket 自动包围auto composition 自动构图auto exposure bracketing 自动包围曝光auto exposure 自动曝光auto film advance 自动进片auto flash 自动闪光auto loading 自动装片auto multi-program 自动多程序auto rewind 自动退片auto wind 自动卷片auto zoom 自动变焦autofocus optics 自动聚焦光学元件automatic exposure(ae) 自动曝光automation/robotics 自动化/机器人技术automation 自动化auxiliary 辅助的Bback light compensation 逆光补偿back light 逆光、背光back 机背background 背景backlighting devices 背光源backplanes 底板balance contrast 反差平衡bar code system 条形码系统barcode scanners 条形码扫描仪barrel distortion 桶形畸变base-stored image sensor (basis) 基存储影像传感器battery check 电池检测battery holder 电池手柄bayonet 卡口beam profilers 电子束仿形器beam splitters 光分路器bellows 皮腔binary image 二值图像只有两级灰度的数字图像（通常为0和1，黑和白）biometrics systems 生物测量系统blue filter 蓝色滤光镜blur 模糊由于散焦、低通滤波、摄像机运动等引起的图像清晰度的下降。

linux 内核编译各个选项的含义

linux 内核编译各个选项的含义linux内核编译各个选项的含义codematurityleveloptions代码成熟度选项表明尚在研发中或尚未顺利完成的代码与驱动.除非你就是测试人员或者开发者,否则切勿挑选generalsetup常规设置localversion-appendtokernelrelease在内核版本后面加上自定义的版本字符串(小于64字符),可以用\-a\命令看到automaticallyappendversioninformationtotheversionstring自动在版本字符串后面添加版本信息,编译时需要有perl以及git仓库支持supportforpagingofanonymousmemory(swap)使用交换分区或者交换文件来做为虚拟内存systemvipcsystemv进程间通信(ipc)积极支持,许多程序须要这个功能.克雷姆斯兰县,除非你晓得自己在搞什么ipcnamespacesipc命名空间支持,不确定可以不选posixmessagequeuesposix消息队列,这就是posixipc中的一部分bsdprocessaccounting将进程的统计信息写入文件的用户级系统调用,主要包括进程的创建时间/创建者/内存占用等信息bsdprocessaccountingversion3fileformat使用新的第三版文件格式,可以包含每个进程的pid和其父进程的pid,但是不兼容老版本的文件格式exporttask/processstatisticsthroughnetlink通过netlink接口向用户空间导出任务/进程的统计信息,与bsdprocessaccounting 的不同之处在于这些统计信息在整个任务/进程生存期都是可用的enableper-taskdelayaccounting在统计信息中包含进程等候系统资源(cpu,io同步,内存交换等)所花费的时间utsnamespacesuts名字空间积极支持,不确认可以说实话auditingsupport审计支持,某些内核模块(例如selinux)需要它,只有同时选择其子项才能对系统调用进行审计enablesystem-callauditingsupport支持对系统调用的审计kernel.configsupport把内核的布局信息编程入内核中,以后可以通过scripts/extract-ikconfig脚本去抽取这些信息enableaccessto.configthrough/proc/config.gz允许通过/proc/config.gz访问内核的配置信息cpusetsupport只有所含大量cpu(大于16个)的smp系统或numa(非一致内存出访)系统才须要它kernel->userspacerelaysupport(formerlyrelayfs)在某些文件系统上(比如说debugfs)提供更多从内核空间向用户空间传达大量数据的USBinitramfssourcefile(s)initrd已经被initramfs取代,如果你不明白这是什么意思,请保持空白编程时优化内核尺寸(采用\而不是\参数编程),有时可以产生错误的二进制代码enableextendedaccountingovertaskstats搜集额外的进程统计数据信息并通过taskstatsUSB发送到用户空间configurestandardkernelfeatures(forsmallsystems)配置标准的内核特性(为小型系统)enable16-bituidsystemcalls容许对uid系统调用展开过时的16-bit外包装sysctlsyscallsupport不需要重启就能修改内核的某些参数和变量,如果你也选择了支持/proc,将能从/proc/sys存取可以影响内核行为的参数或变量loadallsymbolsfordebugging/kksymoops装载所有的调试符号表信息,只供调试时挑选includeallsymbolsinkallsyms在kallsyms中包含内核知道的所有符号,内核将会增大300kdoanextrakallsymspass除非你在kallsyms中辨认出了bug并须要报告这个bug才关上该选项supportforhot-pluggabledevices支持热插拔设备,如usb与pc卡等,udev也需要它enablesupportforprintk容许内核向终端列印字符信息,在须要确诊内核为什么无法运转时挑选bug()support显示故障和失败条件(bug和warn),禁用它将可能导致隐含的错误被忽略enableelfcoredumps内存格式化积极支持,可以协助调试elf格式的程序enablefull-sizeddatastructuresforcore在内核中使用全尺寸的数据结构.禁用它将使得某些内核的数据结构减小以节约内存,但是将会降低性能enablefutexsupport快速用户空间互斥体可以使线程串行化以避免竞态条件,也提高了响应速度.禁用它将导致内核不能正确的运行基于glibc的程序enableeventpollsupport积极支持事件轮循的系统调用usefullshmemfilesystem完全使用shmem来代替ramfs.shmem是基于共享内存的文件系统(可能用到swap),在启用tmpfs后可以挂载为tmpfs供用户空间使用,它比简单的ramfs先进许多usefullslaballocator采用slab全然替代slob展开内存分配,slab就是一种杰出的内存分配管理器,所推荐采用enablevmeventcountersfor/proc/vmstat容许在/proc/vmstat中涵盖虚拟内存事件记数器loadablemodulesupport可加载模块支持enableloadablemodulesupport打开可加载模块支持,如果打开它则必须通过\把内核模块安装在/lib/modules/中moduleunloading容许装载已经读取的模块forcedmoduleunloading允许强制卸载正在使用中的模块(比较危险)moduleversioningsupport容许采用其他内核版本的模块(可能会出来问题)sourcechecksumforallmodules为所有的模块校验源码,如果你不是自己编写内核模块就不需要它automatickernelmoduleloading使内核通过运转modprobe去自动读取所须要的模块,比如说可以自动化解模块的倚赖关系blocklayer块设备层enabletheblocklayer块设备支持,使用硬盘/usb/scsi设备者必选supportforlargeblockdevices仅在采用大于2tb的块设备时须要supportfortracingblockioactions块队列io跟踪支持,它允许用户查看在一个块设备队列上发生的所有事件,可以通过blktrace程序获得磁盘当前的详细统计数据supportforlargesinglefiles仅在可能将采用大于2tb的文件时须要ioschedulersio调度器anticipatoryi/oscheduler假设一个块设备只有一个物理查找磁头(例如一个单独的sata硬盘),将多个随机的小写入流合并成一个大写入流,用写入延时换取最大的写入吞吐量.适用于大多数环境,特别是写入较多的环境(比如文件服务器)deadlinei/oscheduler采用轮询的调度器,简约小巧,提供更多了最轻的加载延后和尚尽如人意的吞吐量,特别适合于加载较多的环境(比如说数据库)cfqi/oscheduler使用qos策略为所有任务分配等量的带宽,避免进程被饿死并实现了较低的延迟,可以认为是上述两种调度器的折中.适用于有大量进程的多用户系统defaulti/oscheduler默认io调度器。

F

F⁃PID复合控制器在闭环光纤陀螺仪中的应用作者：孙亮余震虹（等）来源：《现代电子技术》2013年第13期摘要：陀螺仪是惯性系统的核心部件，目前数字干涉式光纤陀螺（IFOG）以其宽带宽，响应速度快的优点成为首选。

IFOG可以看作一个数字控制系统，因此，其动态性能受控制系统设计的影响。

现根据此类陀螺的工作原理，推导出系统离散传递函数。

将模糊控制器与PID 控制器相结合，设计一种新型的F⁃PID复合控制器取代传统的PID控制器。

仿真结果显示，采用F⁃PID控制器的光纤陀螺系统可以有效地缩短调节时间，减小超调量，并且具有强的抗干扰能力。

关键词： IFOG；数学模型； F⁃PID；抗干扰中图分类号： TN911.7⁃34； U666.1 文献标识码： A 文章编号： 1004⁃373X（2013）13⁃0160⁃04Application of F⁃PID composite controller in closed⁃loop fiber optic gyroscopeSUN Liang， YU Zhen⁃hong， CHEN Hao， XIE Feng⁃feng（College of Internet of Things Engineering， Jiangnan University， Wuxi 214122， China）Abstract： The gyroscope is the core component of inertia system. The interference fibe optic gyroscope （IFOG） has become a fist choice in the area because of its advantages of wide bandwidth and fast response. IFOG can be regarded as a digital control system. Therefore， its dynamic performance is impacted by the design of control system. The discrete transfer function of the control system was deduced according to the working principle of this gyroscope. In combination with Fuzzy controller and PID controller， a new type of F⁃PID compound controller was designed to replace the traditional PID controller. The simulation results show that the fibe optic gyroscope system with F⁃PID controller can effectively shorten the adjustment time， decrease the overshoot，and has also a strong anti⁃interference ability.Keywords： IFOG； mathematical model； F⁃PID； anti⁃interference0 引言随着对陀螺仪尤其是惯导级对精度要求的不断提高，光纤陀螺仪应运而生，并且以其轻小型、低功耗、长寿命、高可靠性、无自锁，可批量化生产等多方面的优势，受到世界许多国家的大学、科研机构尤其是军方的重视，并取得了很大的进步。

一种低成本动态无功补偿装置及两级协同优化运行方法_帅智康

2009年12月电工技术学报Vol.24 No. 12 第24卷第12期TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY Dec. 2009一种低成本动态无功补偿装置及两级协同优化运行方法帅智康罗安吴传平（湖南大学电气与信息工程学院长沙 410082）摘要为实现高能耗企业配电网低成本动态节能，提出了一种兼顾DSTATCOM快速无功补偿及TSC低成本大容量无功补偿优势的混合型无功动态补偿器。

该新型拓扑结构由一台较小容量的DSTATCOM和较大容量的多组TSC构成。

其中，DSTATCOM能实现快速连续无功调节，TSC 实现无功的分级调节，二者协同工作实现低成本快速无级无功调节。

在分析HVC基本工作原理的基础上，本文对HVC的控制方法进行了研究，提出基于专家决策的HVC复合控制策略，确保了HVC快速大容量地进行无功补偿。

同时，本文对多组HVC装置同时工作时的优化运行问题进行了深入研究，提出利用两级协同优化补偿算法，获取各补偿装置的最优投运无功量，实现全局优化节能。

基于上述思想，为某冶炼厂研制了低成本动态节能系统，运行结果表明该系统比传统无功补偿装置节能效果更佳，同时可推广应用于高压配电网。

关键词：动态无功补偿模糊PI控制专家决策协同优化算法低成本中图分类号：TM13；TM76A Novel Low-Cost Dynamic Var Compensator and Two-LevelCollaborative Optimization MethodShuai Zhikang Luo An Wu Chuanping(Hunan University Changsha 410082 China)Abstract To realize energy-saving in distribution grid with low cost, this paper proposes a hybrid var compensator (HVC). The new topology consists of a small capacity DSTATCOM and multi-groups TSC. They work together to realize dynamic var compensation with low cost. Based on the principle analysis of HVC, the control methods of HVC are processed. A compound control strategy based on expert decision is proposed to make sure that the HVC can compensate reactive power with high speed and large capacity. Simultaneously a study about how to optimize when multi-groups HVCs are working is processed in this paper, it proposes by using a new arithmetic called two level coordinated optimization compensation to obtain the optimal reactive power quantity required by any compensation equipment, so as to fulfill the global optimization energy-saving. A dynamic energy-saving system with lost cost has been developed for a melt-factory in northern China, experimental results show that compared to the traditional reactive power compensation equipment, this system can obtain a better effect for energy-saving.Keywords：Dynamic var compensation, fuzzy-PI, expert judgment, collaborative optimization, low cost国家自然科学基金（60774043），国家863计划（2008AA05z211）和高等学校博士点基金（20070532053）资助项目。

智能控制课后仿真

《智能控制》之答禄夫天创作课后仿真陈述院（系）：电气与控制工程学院专业班级：自动化1301班姓名：杨光辉学号：1306050115题目2-3：1ms 进行离散化。

参照专家控制仿真程序，设计专家PID控制器，并进行MATLAB仿真。

专家PID 控制MATLAB仿真程序清单：%Exoert PID Controllerclear all;%清理数据库中所有数据close all;%关闭所有界面图形ts=0.001;%对象采样时间，1mssys=tf(133,[1,25,0]);%受控对象的传递函数dsys=c2d(sys,ts,'z');%连续系统转化为离散系统[num,den]=tfdata(dsys,'v');%离散化后参数，得num和den值u_1=0;u_2=0;%设定初值，u_1是第(k-1)步控制器输出量y_1=0;y_2=0;%设定初值，y_1是第(k-1)步系统对象输出量x=[0,0,0]';%设定误差x1误差导数x2误差积分x3变量初值x2_1=0;%设定误差导数x2_1的初值kp=0.6;%设定比例环节系数ki=0.03;%设定积分环节系数kd=0.01;%设定微分环节系数error_1=0;%设定误差error_1的初值for k=1:1:5000%for循环开始，k从1变更到500，每步的增量为1time(k)=k*ts;%仿真时长[0.0010.5]sr(k)=1.0;%TracingStepSignal系统输入信号u(k)=kp*x(1)+kd*x(2)+ki*x(3);%PIDControllerPID控制器%Expertcontrolrule%Rule1:Unclosedcontrolrule规则1：开环控制if abs(x(1))>0.8%if循环开始，发生式规则，if...then...；误差的绝对值大于u(k)=0.45;%控制器输出量等于elseif abs(x(1))>0.40u(k)=0.40;elseif abs(x(1))>0.20u(k)=0.12;elseif abs(x(1))>0.01u(k)=0.10;end%if循环结束%Rule2规则2if x(1)*x(2)>0|(x(2)==0)%if循环开始，如果误差增大或不变if abs(x(1))>=0.05%内嵌if循环开始，如果误差绝对值大于u(k)=u_1+2*kp*x(1);%控制器输出量施加较强控制else%否则u(k)=u_1+0.4*kp*x(1);%控制器输出量施加一般控制end%内嵌if循环结束end%if循环结束%Rule3规则3if (x(1)*x(2)<0&x(2)*x2_1>0)|(x(1)==0)%if循环开始，如果误差减小或消除u(k)=u(k);%控制器输出量不变end%if循环结束%Rule4规则4if x(1)*x(2)<0&x(2)*x2_1<0%if循环开始，如果误差处于极值状态if abs(x(1))>=0.05%内嵌if循环开始，如果误差绝对值大于u(k)=u_1+2*kp*error_1;%控制器输出量施加较强控制else%否则u(k)=u_1+0.6*kp*error_1;%控制器输出量施加一般控制end%内嵌if循环结束end%if循环结束%Rule5:IntegrationseparationPIcontrol规则5;运用PI控制来消除误差if abs(x(1))<=0.001 %if循环开始如果误差绝对值小于（很小）u(k)=0.5*x(1)+0.010*x(3);%控制器输出量用比例和积分输出end%if循环结束%Restrictingtheoutputofcontroller对控制输出设限if u(k)>=10u(k)=10;%设控制器输出量上限值endif u(k)<=-10u(k)=-10;%设控制器输出量下限值end%LinearmodelZ变更后系统的线性模型y(k)=-den(2)*y_1-den(3)*y_2+num(1)*u(k)+num(2)*u_1+num(3) *u_2;error(k)=r(k)-y(k);%系统误差error的表达式，等于系统输入减去输出%--------Returnofparameters--------%每步计算时的参数更新u_2=u_1;u_1=u(k);%u(k)代替u_1y_2=y_1;y_1=y(k);%y(k)代替y_1x(1)=error(k);%CalculatingP赋误差error值于x1x2_1=x(2);%赋值前步计算时的误差导数X2的值等于X2_1x(2)=(error(k)-error_1)/ts;%CalculatingD求误差导数x2，用于下一步的计算x(3)=x(3)+error(k)*ts;%CalculatingI求误差积分x3error_1=error(k);%赋误差error值于error_1end%for循环结束，整个仿真时长计算全部结束figure(1);%图形1plot(time,r,'b',time,y,'r');%画图，以时间为横坐标，分别画出系统输入、输出随时间的变更曲线xlabel('time(s)');ylabel('r,y');%标注坐标figure(2);%图形2plot(time,r-y,'r');%画r-y,即误差随时间的变更曲线xlabel('time(s)');ylabel('error');%标注坐标专家PID 控制MATLAB仿真程序过程及结果：1.在MATLAB编辑环境下编写专家PID控制仿真程序2.编译运行程序后Figure1：PID控制阶跃响应曲线Figure2：误差响应随时间变更曲线题目3-4：3.27）和（3.28并采取MATLAB进行仿真。

【冬季形容词】怎么样的冬天形容词

【冬季形容词】怎么样的冬天形容词形容词顺序提问者：hman13 |浏览次数：1058次What I would do is to go ___.A. really quietly somewhereB. somewhere quietly reallyC. really quiet somewhereD. somewhere really quiet这道题选什么？为什么？形容词的顺序是怎么排的？请详细解释一下，谢谢！D. somewhere really quietgo somewhere 去某地really 修饰形容词 quiet 的确很安静形容词排列顺序以供参巧：形容词（adjective），简称adj.或a.，是很普遍的一种语言词类。

形容词用来修饰名词，表示人或事物的性质、状态、特征的程度好与坏。

一般情况下，可将形容词分成性质形容词和叙述形容词两类，其位置一般都放在名词前面：1）直接说明事物的性质或特征的形容词是性质形容词，它有级的变化，在句中可作定语等成分。

例如：hot，hotter，hottest，a hot day，“What a fine day!”。

2）叙述形容词只能作表语，所以又称为表语形容词。

这类形容词没有级的变化，也不可用程度副词修饰。

大多数以a开头的形容词都属于这一类。

例如：awake （醒着的），afraid（害怕的），asleep（睡着的）等。

形容词的用法和在句中的排列位置是非常重要的，直接关系着解题的质量。

形容词作的成分是定语，形容词一般放在所修饰的名词之前，如果两个以上的形容词修饰一个名词时，与被修饰名词关系密切的形容词靠近名词。

如果几个形容词的重要性差不多，音节少的形容词在前，音节多的在后。

例如：a powerful socialist country（一个强大的社会主义国家）。

多个形容词修饰一个名词时，其顺序是什么？在目前的语法书中，若有多个形容词修饰名词，则一般排序如下：大小、长短、形状、年龄、新旧、颜色、国籍、出处、材料、用途、类别。

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Compound Fuzzy PID Level Control System Based on WinCC and MATLABQian Linlin1, 2, Li Ping1, Li Hongxing11 Beijing Union University &2 Beijing University of Technology, Beijing, 100101, Chinayxyqll68@Abstract—The s i mulat i on, ver i f i cat i on and real i zat i on of complex control ar i thmet i c are i ntroduced i n deta i l. A compound Fuzzy PID level control system i s real i zed on S emens PROFINET platform. The commun cat on method between supervi si on confi gurati on software Wi nCC 6.0 and eng i neer i ng calculat i ng software MATLAB through OPC technology i s i ntroduced i n th i s paper. Hardware conf i gurat i on and programm i ng are completed by STEP7 prov ded by S emens. Compound Fuzzy PID ar thmet c s reali zed by MATLAB. The programmi ng and calculati ng of complex arithmetic in control system is simplified consumedly. The method could be broadly appli ed i n practi ce systems for i ts s i mple programm i ng, easy real i zat i on and better performance criteria.Keywords- WinCC MATLAB Compound Fuzzy PID OPC technologyI.I NTRODUCTIONAn automatic system of process control is generally composed of Programmable Logic Controllers (PLCs), which are used as controllers, and a Person Computers (PCs), worked as host machines. A PLC manipulates the process variables of the system and a PC supervises the variables and the whole system. Windows Control Center (WinCC), which is a kind of supervision configuration software developed by SIEMENS Company, integrates the functions of supervision, control, data collection and etc. WinCC could effectively supervise and control automatic control devices and production process, but complex control arithmetic couldn’t be easily realized through WinCC. MATrix LABoratory (MATLAB) is a kind of advanced program language used in science engineering calculation. MATLAB has been a basic tool of computer assistant design and analysis in many subject domains because of its strong disposal capability for numerical value and abundant toolboxes. MATLAB is furthermore the preferred platform for arithmetic research and application development in control domain. In the paper, the mode of arithmetic simulation and arithmetic verification is introduced in detail. WinCC is combined with MATLAB in order to control a level system through compound Fuzzy PID arithmetic. The control arithmetic is realized by MATLAB, while data gathering, control function and linking with field devices are achieved by WinCC.II.T HE CONNETION STRUCTURE OF WINCC ANDMATLABWinCC is used as the Server in the system, while the user program of MATLAB as the Client. The Server and the Client are both local machine. WinCC is operated in the foreground, while MATLAB is running in the background. WinCC is according with the protocols of OPC foundation, which includes OPC DA Server, OPC HAD Server and OPC AE Server. As the Server for data access, real-time database could be accessed by OPC DA Server. In the mean time, data could be transferred with other software. MATLAB is a kind of math analysis tool developed by MathWorks CO, which supports OPC technology. After the system model had been established by Simulink, process data could be simulated with MATLAB through its strong background computing capability. The communication between WinCC and MATLAB is shown as figure 1. The programming configuration software STEP7, supervision configuration software WinCC of SIEMENS Company and MATLAB software are installed in supervision computer. Compound Fuzzy PID arithmetic is realized by MATLAB, which is linked with WinCC through OPC interface. The process variables in WinCC are joined with STEP7 by industrial Ethernet and is linked to S7-315 PLC in order to control theFigure 1. The communication between WinCC and MATLABIII.T HE CONFIGURATION OF CONTROLLING ANDSUPERVISINGA.Hardware configurationThe network of control system is based on PROFINET industrial Ethernet technology. The controlled object in this system is a set of two-tank level device. The control systemis composed of a PLC Controller CPU315-2PN/DP , whichis the master station according for PROFINET communication protocol, and two slave stations, which are two remote I/Os˄ET200M˅linked with PROFINET network through proxy IE/PB_LINK. Hardware configuration and programming are accomplished by STEP7 software of Siemens Company. Start-up STEP7 software, and create the station of "SIMATIC 300 Station". Double-This work was supported by the Education Committee Foundationof Beijing China under Grant KM201011417015.2011 Third International Conference on Measuring Technology and Mechatronics Automationclick icon "Hw Config" in browse window so as to open hardware configuration editor. First, place a S-700 rack, then insert CPU315PN/DP, IE/DP_Link, and ET200M into their own slots according to the order numbers of themselves. The hardware configuration of whole system is shown in figure 2. CPU315PN/DP is a controller of Siemens S7-300 series according with PROFINET communication protocol. ET200M is a remote I/O of PROFIBUS-DP protocol, which embodies IM151-1 communication module. IE/PB_LINK is the DP/PA converter linking between PROFINET modules and PROFIBUS-DP modules. Open the “Property” dialog box of AI/AO module, and modify the types of input and output into standard current signal 4~20mA. Double-Click icon of proxy “IE/PB_LINK”, its “Property” dialog box popups. The name of IE/PB_LINK could be written in “Drive name”, and “device number” should be chosen. Pay attention to avoid the confliction of respect “device number”.[1]Figure 2. The hardware configuration of the systemB.Setting communication interfaceAfter hardware configuration, the communication channel between STEP7 and controller should be set. Open control panel, then change the interface path of “Set PG/PC Interface”. As shown in figure 3, select “TCP/IP ėRealtek RTL8139(A) PCI Fast Ethernet Adapter” option, then the hardware configuration of system could be downloaded into controller CPU315PN/DP through the channel and debugged.[1]Figure 3. The selection of communication channel.C.The programming and debugging of systemSystem control programs are written by STEP7 software. Programs could be created in the standard languages Ladder Logic (LAD), Statement List (STL) or Function Block Diagram (FBD) with STEP7. User programs are generally composed of organization blocks (OB), Function blocks (FB), Functions(FB), Data blocks(DB), and etc. There are three kinds of programming mode according to the complexity of process control, which are linearization programming, modularization programming and configuration programming. In an actual process control system, user programs are written in term of its own strategy. The control programs are compiled and downloaded to S7-315 CPU. A variable table could be established by STEP7, which could debug programs, modify parameters and monitor variables on line.D.The supervision configurationOpen WinCC software, and create a new supervising project ˈas shown in figure 4. Click variable manager with the right key of mouse to append a new driver program. Choose “SIMATIC S7 PROTOCOL SUITE” program in the list of folder “bin” and open it, which fulfills the new driver. Select “Industrial Ethernet” in upper driver, click right key to complete the program link between STEP7 and WinCC. The needed variables, such as set point (SP), level process value (PV) and so on, could be established in right browser and joined with the ones set in STEP7. The supervising chart, variable trend graph and alarm picture could be configuredin WinCC. [2]Figure 4. WinCC supervision configuration graphIV.T HE REALIZATION OF COMPOUND F UZZY PIDARITHMETICpound Fuzzy PID arithmeticFuzzy control theory has been rapidly developed since it came into being in 1965. Some complex plants, such as steel-making furnace, cement stove, locomotive, home electric appliances and the like, had been early or late controlled by Fuzzy arithmetic to some extent, which had obtained better control effect. Usually, there two inputs in general Fuzzy controller, which are error () and theedifferential coefficient of error (). The essence of general Fuzzy control is namely a non-liner PD (proportional-differential) control, which has relatively lower steady-state precision.eIn order to improve the steady-state precision of upper Fuzzy control, the language values for language variable should be increased. Nicer partition of language value could result in better performance. But the quantities of rules and calculating in the system would increase largely. Fuzzy control table would be difficult to grasp, the debug of system would be more troublesome and the real-time capability of system couldn’t be reached. [3]A method of resolve upper contradiction is to adopt piecewise control with different control modes. Piecewise control arithmetic with more than one mode integrates the strong points of proportional control, Fuzzy control and integral control. This kind of control method could bring up rapid response speed and robust capability against the change of system parameters, which manipulates the system through compound Fuzzy PID arithmetic, also called high precision proportional-Fuzzy-PI arithmetic. Its structure graph is shown in figure 5. [3]Figure 5. The structure graph of proportional-Fuzzy-PI controlB.The realization of control arithmetic by MATLABMATLAB is the special software provided by MathWorks, which has strong functions on analyzing data, disposing matrix and plotting real-time curve. Compound Fuzzy PID arithmetic is programmed by M language of MATLAB. The arithmetic is simulated on level plant, which transfer function has been identified through step response method. There is no interacting between the three modes because they are used in respective subsection and debugged separately. In compound Fuzzy PID arithmetic, the setting of switch threshold value is very important. The threshold value should be most felicitous so that system could smoothly switch from proportional mode to Fuzzy mode. If the value was bigger, system will come into Fuzzy mode prematurely, which would reduce response speed and attenuate overshoot. If threshold value was less, system will switch into Fuzzy mode when extremely approaching target value, which would result bigger overshoot. The optimized threshold value of system should be chosen through numerous experiments according to the characters of simulation object. The well-done program of compound Fuzzy PID control arithmetic will be worked as S-function in MATLAB, which could be called in Simulink file during the communication between MATLAB and WinCC.V.T HE COMMUNICATION BETWEEN MATLAB ANDW IN CCThere is an OPC tool kit in MATLAB 7.0 and its advanced edition. MATLAB could communicate with OPC server and read the variables in it. The OPC tool kit has been used in this system for the linking between MATLAB and OPC server. So MATLAB could access real-time process variables and dominate the parameters in system. The realization of communication includes two parts, which areM-file and Simulink module. The function of M-file is to establish the connection between MATLAB and WinCC, which permits MATLAB access process variables and call sub-program by their changing values. The use of Simulink module is to create needed process model, which reads the value of process variables of WinCC, completes calculations and sends back to variables of WinCC. [4]Firstly, establish the connection between the OPCServerof MATLAB and WinCC. In this level control system, the needed data acquisition includes level value “ENG INEERING”, set-point value “SETPOINT” and the opening of controlled valve “CONTROL”. The main M-file used for the connection with WinCC programmed by MATLAB, and its explanation is as following:hostinfo = opcserverinfo(’localhost’);da = opcda(’localhost’, ’OPCServer WinCC’);connect(da); %connect OPCServergrp = addgroup(da ’group1’); % add grouplevel = additem(grp ’ENGINEERING’); % add itemset-point = additem(grp ’SETPOINT’);valve = additem(grp ’CONTROL’);set(grp ’Updaterate’, 0.5); % set access intervalstart(grp); % record beginThen the Simulink model could be created in MATLAB. The model is constructed by OPC tool kit in MATLAB, which could communicate with WinCC 6.0. WinCC software is the OPC server in this system. “OPC Config Real-time” model, “OPC Read” model and “OPC Write” model are placed in Simulink file. They are linked each other as shown in figure 6. A WinCC 6.0 OPC server is appendedin “OPC Config Real-time” model, while communication must be configured when WinCC has been activated. Set point, which item ID is “SETPOINT”, and level process value, which item ID is “ENGINEERING”, should be addedin “OPC Read” model. Then the output of controller, which item ID is “CONTROL”, should be added in “OPC Write” model. That completes the data linking setting between WinCC 6.0 and MATLAB. [5]Figure 6. Simulink model file of system data disposalVI.T HE ANALYSIS OF CONTROL EFFECTLevel process value could be displayed on WinCC through online trend control when system is running. figure 7 indicated the trend curve of Level process value, which control arithmetic is conventional PI. After adjusting the proportional gain and integral time constant of PI controller, a better step response is achieved. Although its setting time is shorter, its maximum overshoot is larger. In the meantime,its capability of anti-interference is poor.Figure 7. The level trend curve of conventional PIFigure 8 indicated the trend curve of Level process value, which control arithmetic is compound Fuzzy PID. The control effect indicated that there almost no maximum overshoot in the step response of compound Fuzzy PID arithmetic. In the mean time, this arithmetic has a strong capacity to resist interference. So it’s a better arithmetic torealize intelligent control.Figure 8. The level trend curve of compound Fuzzy PID ConclusionVII.CONCLUSIONThe communication method between supervising configuration software WinCC 6.0 and MATLAB software realized by OPC technology is discussed in the paper. Different complex control arithmetic could be completed in control system through function calling between WinCC 6.0 and MATLAB. The programming and calculating of complex arithmetic in control system would be simplified consumedly, which results the broad application of MATLAB in practice systems. The method could be programmed simply, realized easily and its performance criteria could meet system demand.R EFERENCES[1]Siemens AG , “Configuring Hardware with STEP7” (manual), 6ES7810-4CA08-8BW0, 2006(3).[2]Siemens AG , “WinCC_Communication_Manual2” (manual), 6AV6 392-1CA05 0AH0, 1999(9).[3]Pan Xinmin, Wang Yanfang, “Applied course on microcomputer control technology”, Beijing: Publishing House of Electronics Industry, 2008(5): 273-274. (in Chinese)[4]Li Xiangyu, Qian Yu, “The integration of MATLAB and WinCC based on OPC”, Beijing: Control & Automation, 2007(11): 297-299. (in Chinese)[5]Zhang Weibo, Han Baocun, “An intelligent level control system based on MATLAB and WinCC”, Heilongjiang: Techniques of Automation & Applications, 2008(11): 36-39. (in Chinese)。