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Towards a robust BCI:Error potentials and online learningAnna Buttﬁeld,Pierre W.Ferrez and Jos´e del l´a nAbstractRecent advances in theﬁeld of Brain-Computer Interfaces(BCIs)have shown that BCIs have the potential to provide a powerful new channel of communication,completely independent of muscular and nervous systems.However,while there have been successful laboratory demonstrations,there are still issues that need to be addressed before BCIs can be used by non-experts outside the laboratory.At IDIAP we have been investigating several areas that we believe will allow us to improve the robustness,ﬂexibility and reliability of BCIs.One area is recognition of cognitive error states,that is,identifying errors through the brain’s reaction to mistakes.The production of these error potentials(ErrP)in reaction to an error made by the user is well established.We have extended this work by identifyinga similar but distinct ErrP that is generated in response to an error made by the interface,(a misinterpretation of acommand that the user has given).This ErrP can be satisfactorily identiﬁed in single trials and can be demonstrated to improve the theoretical performance of a BCI.A second area of research is online adaptation of the classiﬁer.BCI signals change over time,both between sessions and within a single session,due to a number of factors.This means that a classiﬁer trained on data from a previous session will probably not be optimal for a new session.In this paper we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase.We also discuss the future direction of this research,including the combination of these two currently separate issues to create a potentially very powerful BCI.Index TermsBrain-computer interface,Cognitive error state recognition,Online learning,Adaptive classiﬁersI.I NTRODUCTIONThe goal of a brain-computer interface(BCI)is to create a direct channel of communication between a user’s brain and a computer,completely bypassing the traditional muscle-dependent communication channels.This is still a very youngﬁeld of research and it encompasses many different approaches to a variety of problems.The BCI system that we have developed at the IDIAP Research Institute has shown good results in distinguishing between up to three mental states,including imagination of left and right hand movements,3D visualisation andThis work is supported by the European IST Programme FET Project FP6-003758and by the Swiss National Science Foundation NCCR “IM2”.This paper only reﬂects the authors’views and funding agencies are not liable for any use that may be made of the information contained herein.Authors are with the IDIAP Research Institute,CH-1920Martigny,Switzerland(e-mail:{anna.buttﬁeld,pierre.ferrez,lan}@idiap.ch).language tasks[1].We use a Gaussian mixture classiﬁer to distinguish between the given tasks,which allows us to deﬁne an“unknown”output when the probability of none of the deﬁned classes is above a deﬁned conﬁdence threshold,effectively creating an“idle”state without having to model it directly.While this technology has been demonstrated in the laboratory,it is not yet ready to be taken out of the laboratory and used in real-world situations. Our work at the moment is focussing on different ways of improving the robustness of BCIs with the goal of making BCIs a more practical and reliable technology.One avenue of research that we are investigating is the use of high-level cognitive and emotional information in BCIs.In particular we are looking at error potentials(ErrP),the neural correlates to error awareness.The appearance of error potentials in response to errors made by the user are well established.Our research has identiﬁed a new type of error potential that is generated in response to errors made by the BCI rather than the user.In addition to identifying errors and stopping the BCI from executing incorrect commands,this new type of error potential may provide us with performance feedback that could allow us to improve the performance of the classiﬁer while it is being used.Online learning is another issue that we are currently investigating.EEG signals naturally change over time,both between different sessions and within a single session.Online learning can be used to adapt the classiﬁer throughout its use and keep it tuned to drifts in the signals it is receiving in each session.Our hypothesis is that online learning used during initial training will reduce training time by facilitating mutual adaptation between the user and the BCI,allowing the user to reﬁne his or her mental strategy through rapid feedback.During ongoing use we believe we can improve the performance of the classiﬁer by constantly tuning it with alternative learning paradigms that do not require explicit knowledge of the target class.However,dynamic adaptation of the BCI is risky,since we may confuse the user by rapid and unpredictable changes,or degrade the performance of the classiﬁer through inappropriate adaptation.In this paper we will be describing the current state of the IDIAP BCI,including the hardware and statistical classiﬁer.We will also present our current research in the two areas outlined above,error potentials and online learning.II.T HE IDIAP BCIWe have recently shown that after a few days of training,subjects are able to control a miniature robot in an indoor environment with several rooms and corridors using mental commands derived from an EEG-based BCI[1]. Key aspects that make it possible are the use of an asynchronous BCI and the combination of the user’s high-level commands with advanced robotics that implement those commands efﬁciently.We are working to improve this initial demonstrator,in collaboration with several European institutions,along four lines.Theﬁrst is the development of a more powerful adaptive shared autonomy framework for the cooperation of the human user and the robot in achieving the target.The second line is the use of a technique recently developed by Grave et al.[2]that estimates the local ﬁeld potentials(LFP)in the whole human brain from scalp EEG.Recent results show signiﬁcant improvements in the classiﬁcation of bimanual motor tasks using estimated LFP with respect to scalp EEG[3].The third and fourthresearch avenues seek to improve the robustness of a BCI and will be discussed in this paper.A.Hardware and signal acquisitionEEG potentials are acquired with a portable BioSemi system using a cap with either32or64integrated electrodes arranged in the modiﬁed10/20International System[4].The EEG recordings are monopolar and taken at512Hz. The common average reference(CAR)procedure is used to suppress the background brain activity,where at each time step the average potential over all the channels is subtracted from each channel.This re-referencing procedure removes the background activity,leaving activity from local sources beneath the electrodes.B.Statistical classiﬁerThis is a short summary of the classiﬁer we use in the IDIAP BCI.For more details,see[1].We use a Gaussian classiﬁer to separate the signal into the different classes of mental task.Each class is represented by a number of Gaussian prototypes,typically less than four.That is,we assume that the class-conditional probability function of class C k is a superposition of N k Gaussian prototypes.We also assume that all classes have equal prior probability. All classes have the same number of prototypes N p,and for each class each prototype has equal weight1/N p. Thus,the activity a ikof the i th prototype of class C k for a given sample x is the value of the Gaussian with centreµi k and covariance matrixΣik.From this we calculate the posterior probability y k of the class C k.The posteriorprobability y k of the class C k is now the sum of the activities of all the prototypes of class k divided by the sum of the activities of all the prototypes of all the classes.The input vector x can be composed of either temporal or frequency features from a selection of electrodes,depending on the experiment.The classiﬁer output for input vector x is now the class with the highest probability,provided that the probability is above a given threshold,otherwise the result is“unknown”.This rejection criteria gives the BCI theﬂexibility to not make a decision at any point without explicitly modelling an idle state.Usually each prototype of each class would have an individual covariance matrixΣik,but to reduce the number of parameters the model has a single diagonal covariance matrix common to all the prototypes of the same class. During ofﬂine training of the classiﬁer,the prototype centres are initialised by a clustering algorithm,generally self-organising maps[5].This initial estimate is then improved by stochastic gradient descent to minimise the meansquare error E=12k(y k−t k)2,where t is the target vector in the form1-of-C;that is,if the second of threeclasses was the desired output,the target vector is(0,1,0).The covariance matrices are computed individually then averaged over the prototypes of each class to giveΣk.III.E RROR P OTENTIALSEEG-based BCIs are prone to errors in the recognition of the user’s intent.However,EEG signals provide us with a tool to help us overcome this problem.In addition to the command signals generated intentionally by the user,EEG signals carry high level information about the cognitive states of the user.These states include attention,fatigue and motivation as well as the one we are investigating—error recognition,where the user“reacts”to the occurrence of an error.Brain signals associated with the recognition of an error made by the user are well established[6],[7],[8].“Response ErrP”occur when the user makes an error and recognizes it immediately[6],[7].This situation can be provoked by asking a user to perform a task that requires fast reactions so that the user makes some mistakes.A few studies have addressed the recognition of this kind of ErrP in single trials as a potential tool to improve the performance of a BCI[9],[10].A second type of error potentials has also been identiﬁed.Known as“Feedback ErrP”,they occur when a user makes an error but is unaware of it until he or she is informed by feedback[8]. The brain response in this case is similar to that of response ErrP,but is a reaction to the feedback rather than the incorrect action.In both cases the timing of the brain reaction is tied to the point where the user realises his or her mistake,be that at the time of action or on receipt of negative feedback.An important aspect of the described ErrP(response ErrP and feedback ErrP)is that they always follow an error made by the user.First the user makes a selection,and then the ErrP arise either after the occurrence of an error or after a feedback indicating the error.In the context of a BCI,the central question is whether ErrP are also elicited when the interface makes an error in recognising the user’s intent.Schalk et al.[11]have previously observed that such an ErrP appears in a BCI when the cursor reaches wrong targets,an operation that requires a number of consecutive mental commands.Here we are interested in the recognition of such a kind of ErrP in single trials—i.e.,after the user delivers each mental command.A.Experimental SetupIn a previous study,we presented experimental results showing the presence of ErrP in response to an error made by the interface rather than the user[12].In the three subjects studied we were able to identify these“Interaction ErrP”on a single-trial basis,which is crucial for their integration into a real-time BCI.In these subjects were were able to identify to presence of the ErrP in the error trials with an average accuracy of79.9%and the abscence of this ErrP in correct trials with an average accuracy of82.4%.The results of this study are presented and discussed further here.The experiment simulated a real interaction with a robot,where the user gives repetitive commands to bring the robot to the left or right side of a room.Feedback is delivered by two progress bars showing the number of times each command has been delivered.In order to separate the complex usage of the BCI from the reaction to the error,the task was presented in a simpliﬁed manual form.Instead of using a BCI to deliver the commands, the user presses a key to issue the command.By removing the BCI classiﬁer from this part of the experiment and making the user task trivial,we ensure that the user performance is perfect and the only mistakes are the deliberately introduced mistakes made by the interface.So,the user repeatedly presses a key to issue his or her command(without making mistakes,due to the triviality of the task),but20%of the time the interface makes a mistake and provides the wrong feedback.Three healthy male subjects between the ages of24and42participated in this experiment.The EEG signals were recorded with a32electrode cap,but only channels Cz and Fz wereused for classiﬁcation since this combination produced the best results.This selection of electrodes makes sense because the distribution of ErrP is known to be fronto-central along the midline.The signals were ﬁltered with a 1-10Hz bandpass ﬁlter,since error potentials are known to be relatively slow cortical potentials.The classiﬁer then considers the 0.5second window starting between 150ms and 650ms after the feedback.The input to the classiﬁer is a 128element vector of the temporal features (0.5seconds at 128Hz for Cz and Fz).B.Experimental ResultsExamining the average difference error-minus-correct (Figure 1)of our experimental results reveals what seems to be a new kind of ErrP,different from “Response”and “Feedback”ErrP,which for convenience we call “Interaction ErrP”.A ﬁrst sharp negative peak occurs 270ms after the feedback and is followed by a positive peak (between 350and 450ms after the feedback)and by a later negative peak (∼550after the feedback).Since our protocol is quite similar to an oddball paradigm the question arises of whether the potentials we describe are simply oddball N200and P300.To clarify this issue we ran a second experiment with an error rate of 50%,where error trials are no less frequent than correct trials.The potentials observed in this case have the same timing as those observed in the case of 20%(Figure 2).This means that while we cannot exclude the possibly that N200and P300contribute when the error rate is 20%,the oddball N200and P300are not sufﬁcient to explain the reported potentials.Time [s]A m p l i t u d e [µV ]Fig.1.Average EEG for the difference error-minus-correct for channel Cz for the three subjects plus the grand average of them for 20%error.Feedback is delivered at time 0seconds.The negative and positive peaks show up about 270ms and between 350and 450ms after the feedback,respectively.A later negative peak shows up about 550ms after the feedback.For ErrP to be useful in a real-time BCI,we need to be able to identify error potentials in single trials,not just in grand averages.Table I gives the classiﬁcation rates of error and correct single trials in a 10-fold cross-validation study.These classiﬁcation rates can be demonstrated to improve the theoretical performance of a BCI in terms of bit rate.The bit rate is a measure of performance that represents the amount of information communicated per unitTime [s]A m p l i t u d e [µV ]Fig.2.Grand average EEG of two subjects showing the difference error-minus-correct for channel Cz when the error rate is 50%.Feedback is delivered at time 0seconds.TABLE IC LASSIFICATION RATES OF EACH SUBJECT ON ERROR AND CORRECT SINGLE TRIALS .Subject 1Subject 2Subject 3Average Error trials [%]87.3±11.374.4±12.478.1±14.879.9±6.6Correct trials [%]82.8±7.275.3±10.089.2±4.982.4±7.0time.It is generally expressed in bits per trial.Traditional bit rate measures can be adapted to model the integration of error potentials into a BCI [12].The most general method of integrating the error potentials is to stop the execution of a command when an error potential is detected.Alternatively,in the speciﬁc case of a 2-class BCI we can replace a command with the other command when we detect an error potential.Using these bit rate equations we can calculate the theoretical performance of a BCI,assuming that the accuracy of the BCI is 80%and the subject-speciﬁc ErrP recognition rates are as reported in Table I.The performance of the BCI integrating ErrP is compared with that of standard 2-class and 3-class BCIs,which have performances of 0.28and 0.66bits per trial respectively.Table II shows that we can achieve a signiﬁcant increase in the bit rate by stopping the commands identiﬁed as erroneous,with an average gain over the standard BCI of 72%in the case of a 2-class BCI and 28%in the case of a 3-class BCI.Surprisingly,the average gain from replacing the erroneous commands in a 2-class BCI is much lower,14%,and the performance of subject #2actually decreases under this paradigm.IV.O NLINE LEARNINGFor a BCI to be an effective tool it must have the ability to adapt dynamically throughout its use.The EEG signals received by the BCI will always change over time,both within a single session and between sessions,dueTABLE IIP ERFORMANCES OF THE BCI INTEGRATING E RR P FOR THE3SUBJECTS AND THE AVERAGE OF THEM.Subjects#1#2#3AverageN c=3BpT0.910.730.920.85stop Increase37.0%9.5%38.0%27.6%N c=2BpT0.530.400.520.48stop Increase90.9%41.9%85.5%71.7%N c=2BpT0.360.190.440.32replace Increase28.9%-31.5%58.9%14.2%to a number of factors.These factors include change in strategy by the user,user fatigue,and small differences in electrode position.So while at the beginning of each session the classiﬁer will be initialised with respect to previous sessions,it must adapt itself to the particular signals it is receiving in the current session.However,this adaptation must be undertaken with great care—rapid and unpredictable changes in the classiﬁer will confuse the user,and incorrect adaptation will degrade the performance of the classiﬁer.Work so far has focused on online learning during the training phase,where the user is told which command to give and so the real target is always known,making it a supervised learning task.The goal is to reduce the time a user takes to learn to use the system efﬁciently by facilitating mutual adaptation between the user and the BCI. This means that the user can reﬁne his or her mental strategies while receiving rapid feedback from the BCI.Once the user has been trained and can produce the BCI commands reliably,he or she is ready to move to unstructured, unsupervised tasks such as controlling a robot.In this situation the techniques described below could be used in short recalibration sessions.Previous preliminary work on this task[13]evaluated the advantages of using continued online learning with the basic gradient descent algorithm on the Gaussian classiﬁer.Since then we have been investigating extensions of the basic gradient descent algorithm such as stochastic meta descent[14],which accelerates training by adapting individual learning rates for each parameter of the classiﬁer.A.Stochastic Meta DescentStochastic Meta Descent(SMD)[14]is an extension of gradient descent that uses adaptive learning rates to accelerate learning.The SMD algorithm is applied to each parameter in the classiﬁer separately(the centre and covariance of each Gaussian prototype),and each parameter maintains and adapts an individual learning rate.This is in contrast to basic gradient descent,which uses a single learning rate for all parameters.The details of our implementation of SMD can be found in[15].This approach has the potential for faster adaptation,but requires more computation.It also has more parameters that need to be tuned—basic gradient descent has two parameters, learning rates for the centres and covariances of the prototypes,while SMD has four parameters,initialisation values for the learning rates as well as an additional hyper-parameter.B.Experimental resultsInitial ofﬂine experiments have been performed on data recorded while giving no feedback to the subjects and no online learning was used during recording.We tested these algorithms on a three-class problem(imagination of left and right hand movements,and vocabulary search).Each class was performed for one second before switching to a different class in random order.Data is from three subjects,each with four sessions of almost four minutes collected with a break of ten minutes between sessions.Samples are passed to the classiﬁer16times per second and the output from eight samples is averaged to give a decision every0.5seconds.The features vector used for classiﬁcation was constructed by estimating the power spectral density over the previous second in the frequency range8-30Hz at2Hz resolution for the8centro-parietal channels(C3,Cz,C4,CP1,CP2,P3,Pz,and P4),giving a96element feature vector.In this experiment we measured how well the classiﬁer tracked the changing signals by applying online learning through all the sessions and measuring the classiﬁcation performance.For each subject,the classiﬁer wasﬁrst trained ofﬂine on the data from theﬁrst session.The resulting classiﬁer was then applied to sessions two through four,with theﬁnal adapted classiﬁer from the end of the previous session used as the initial classiﬁer for the next session.In these sessions we are continually adapting the classiﬁer—each new sample comes in,is classiﬁed with the existing classiﬁer,and the classiﬁer is updated based on this sample.We compared basic gradient descent and the SMD algorithm against the static classiﬁer with no adaptation,where the classiﬁer trained on theﬁrst session is used to classify the following sessions with no further modiﬁcation.Preliminary tests of basic gradient descent over a range of learning parameter values showed that the optimal parameters vary between subjects and sessions.We selected learning parameter values(the same for all sessions of all subjects)for gradient descent.This same value also serves as the initialisation value for SMD.In these experiments both gradient descent and SMD outperform the static classiﬁer.Figure3shows the performance over time of the different classiﬁers for the three sessions of the second.It can be seen that the classiﬁcation rates of the adaptive algorithms are statistically signiﬁcantly better than the static classiﬁer,with SMD better than gradient descent,especially towards the end of each session.Similar trends are observed with the other subjects.In addition to applying the online learning algorithms throughout the session,we wanted to see whether there is a performance gain in applying the online learning algorithms for theﬁrst half of the session only,then applying the resultant classiﬁer to the second half of the session with no further learning.This is similar to a recalibration scenario,where we want to use supervised learning for only part of the session.Results from this experiment show that there is a small improvement when using the classiﬁer trained on theﬁrst half of the data over the classiﬁer with no further training,but there is no statistically signiﬁcant difference between the performance of basic gradient descent and SMD.Table III shows the classiﬁcation results for the three subjects averaged over their three sessions,and the overall average.The results are given as the percentage improvement of the online learning algorithms over the static classiﬁer.There are three parts:the improvement in theﬁrst half of the data,the improvement in the second half1234567891050100Subject 2 − Session 2Subject 2 − Session 3Fig.3.Percentage of trials correctly classiﬁed in 20second bins for the static classiﬁer,basic gradient descent and SMD.Both of the adaptive algorithms have signiﬁcantly better performance than the static classiﬁer,with SMD performing better than basic gradient descent,especially towards the end of each session.of the data when the online learning is continued,and the improvement on the second half of the data when the classiﬁer is only trained on the ﬁrst half of the data.Although there is variation between the subjects,the ﬁgures hold to the general trend as discussed above.TABLE IIIA VERAGE PERCENTAGE IMPROVEMENT OF ONLINE LEARNING ,SMD AND BASIC GRADIENT DESCENT (GD)OVER STATIC CLASSIFIER First halfSecond half Adapting Not adapting Subject 1GD 3.57.9 3.9SMD 4.99.3 5.9Subject 2GD 6.17.8 3.1SMD 9.318.6 5.2Subject 3GD18.318.77.1SMD28.033.0 2.5Average GD9.311.5 4.7SMD 14.020.3 4.5V.C ONCLUSION AND FUTURE WORKThe two issues discussed in this paper,error potentials and online learning,have shown promising results in their early stages of investigation.Work needs to be done to integrate these principles into the BCI system.Operating individually these research areas can improve the performance of the bined,they open up a new area ofresearch—continued reinforcement learning during general usage of the BCI using error potentials as feedback on the performance of the BCI.This means that for every decision the BCI makes we do not know what the correct decision should have been,but we are informed by the presence of the error potential when a wrong decision has been made,and we can use this information to adapt the classiﬁer.Another area of research is the use of estimates of the localﬁeld potentials(LFP)to improve the classiﬁer[2][3]. This technique allows us to generate a three dimensional map of the activity of from the EEG signals.Through this technique we can also gain a better understanding of the nature of the brain activity driving the BCI.R EFERENCES[1]J.del l´a n,F.Renkens,J.Mouri˜n o,and W.Gerstner,“Non-invasive brain-actuated control of a mobile robot by human EEG,”IEEETrans.Biomedical Engineering,vol.51,pp.1026–1033,2004.[2]R.Grave de Peralta,M.Murray,C.Michel,R.Martuzzi,and S.Gonzalez Andino,“Electrical neuroimaging based on biophysicalconstraints,”NeuroImage,vol.21,pp.527–539,2004.[3]R.Grave de Peralta,S.Gonzalez Andino,L.Perez,P.Ferrez,and J.del l´a n,“Non-invasive estimation of localﬁeld potentials forneuroprosthesis control,”Cognitive Processing,vol.6,pp.59–64,2005.[4] F.Sharbrough,G.Chatrian,R.Lesser,H.Lders,M.Nuwer,and W.Picton,“Am.electroencephalogr.society guidelines for standardelectrode position nomenclature,”Clin.Neurophysiol.,vol.8,pp.200–202,1991.[5]T.Kohonen,Self-Organising Maps,2nd ed.Berlin:Springer-Verlag,1997.[6]W.Gehring,M.Coles,D.Meyer,and E.Donchin,“The error-related negativity:An event-related brain potential accompanying errors,”Psychophysiology,vol.27,1990.[7]M.Falkenstein,J.Hoormann,S.Christ,and J.Hohnsbein,“ERP components on reaction errors and their functional signiﬁcance:Atutorial,”Biological Psychology,vol.51,pp.87–107,2000.[8] C.Holroyd and M.Coles,“The neural basis of human error processing:Reinforcement learning,dopamine,and the error-related negativity,”Psychological Review,vol.109,pp.679–709,2002.[9] B.Blankertz,G.Dornhege,C.Sch¨a fer,R.Krepki,J.Kohlmorgen,K.-R.M¨u ller,V.Kunzmann,F.Losch,and G.Curio,“Boosting bit ratesand error detection for the classiﬁcation of fast-paced motor commands based on single-trial EEG analysis,”IEEE Trans.Neural Systems and Rehabilitation Engineering,vol.11,pp.127–131,2003.[10]L.Parra,C.Spence,A.Gerson,and P.Sajda,“Response error correction—A demonstration of improved human-machine performanceusing real-time EEG monitoring,”IEEE Trans.Neural Systems and Rehabilitation Engineering,vol.11,pp.173–177,2003.[11]G.Schalk,J.Wolpaw, D.McFarland,and G.Pfurtscheller,“EEG-based communication:Presence of an error potential,”ClinicalNeurophysiology,vol.111,pp.2138–2144,2000.[12]P.Ferrez and J.del l´a n,“You are wrong!—Automatic detection of interaction errors from brain waves,”in Proc.19th Int.JointConf.Artiﬁcial Intelligence,2005.[13]J.del l´a n,“On the need for on-line learning in brain-computer interfaces,”in Proc.Int.Joint Conf.Neural Networks,2004.[14]N.Schraudolph,“Local gain adaptation in stochastic gradient descent,”in Proc.9th Int.Conf.Artiﬁcial Neural Networks,1999.[15] A.Buttﬁeld and J.del l´a n,“Online classiﬁer adaptation in brain computer interfaces,”IDIAP,IDIAP Research Report06-16,2006.[Online].Available:http://www.idiap.ch/publications.php。

c++编程报错error的解决方法

文章标题：深入探讨C++编程中报错error的解决方法在C++编程中，我们经常会遇到各种各样的报错信息，这不仅是初学者的困扰，即便是经验丰富的程序员也会遇到各种报错情况。

在本文中，我将为您详细解读C++编程中常见的报错类型，并提供解决方法，帮助您更好地理解和应对这些问题。

1. 编译错误编译错误是在编译期间出现的错误，通常指程序无法通过编译器的检查，无法生成可执行文件。

常见的编译错误包括语法错误、语义错误和类型错误。

我们可以通过查看编译器的报错信息，逐行检查代码，找出并修复错误所在。

2. 运行时错误运行时错误是指程序在运行过程中出现的错误，导致程序异常终止或产生错误结果。

常见的运行时错误包括空指针引用、数组越界访问、未捕获的异常等。

我们应该在编写代码时加入适当的防御性编程，对可能出现的异常情况进行处理和容错。

3. 逻辑错误逻辑错误是指程序在逻辑上有误，导致程序无法按照预期的逻辑执行。

通常这类错误不会导致程序崩溃，但会导致程序输出错误的结果。

在遇到逻辑错误时，我们可以通过调试工具逐步执行程序，定位错误所在，并修复逻辑错误。

4. 解决方法针对不同类型的报错，我们可以采取相应的解决方法。

在面对编译错误时，我们需要仔细查看编译器的报错信息，逐行检查代码，修复语法、语义和类型错误；对于运行时错误，我们需要在代码中加入适当的异常处理和错误检测机制，确保程序能够处理各种异常情况；而在面对逻辑错误时，则需要通过调试工具逐步执行程序，找出逻辑错误的根源并加以修复。

总结与回顾通过本文的内容，我希望您能更清晰地理解C++编程中常见报错的类型和解决方法。

无论是编译错误、运行时错误还是逻辑错误，都不是令人畏惧的难题，只要我们用心对待，认真分析和解决问题，就能够在编程的道路上走得更远。

个人观点与理解在我看来，C++编程中遇到报错并非坏事，反而可以帮助我们发现程序中潜在的问题，提高代码的质量和健壮性。

通过及时解决报错问题，我们能够更好地理解代码运行的机制，提升自己的编程水平。

error计算公式

error计算公式
"error"通常指的是误差，计算误差的公式可以根据具体情况而定。

一般来说，误差可以用以下公式来计算：
绝对误差 = | 真实值测量值 |。

相对误差 = (| 真实值测量值 | / | 真实值 |) 100%。

在这里，"真实值"是指被测量的真实数值，"测量值"是指实际测量得到的数值。

绝对误差表示了测量值与真实值之间的差距，而相对误差则表示了这个差距相对于真实值的大小。

这两种误差计算方法能够帮助我们评估测量的准确性，并且在科学实验、工程领域以及其他需要精确测量的领域中有着广泛的应用。

除了绝对误差和相对误差之外，还有其他一些误差计算方法，比如均方根误差（RMSE）等，这些方法适用于不同的情况和需求。

在实际应用中，我们需要根据具体情况选择合适的误差计算方法，以确保我们对测量结果的准确性有一个清晰的认识。

realtime error 的处理

realtime error 的处理"realtime error" 并不是一个特定的错误信息，而是一个泛指的术语，表示在实时系统或应用程序中发生的错误。

实时系统通常要求在特定的时间范围内产生响应，因此错误的处理方式需要考虑实时性的要求。

以下是一些处理实时错误的一般性建议：
1. 错误日志和报警：在实时系统中，记录错误日志对于排除问题和系统监控至关重要。

当发生错误时，及时记录错误信息，并发送报警通知相关人员。

2. 异常处理：在代码中使用适当的异常处理机制，以便在发生错误时能够进行优雅的处理。

这可能包括捕获异常、记录错误信息，并采取适当的措施，例如进行系统恢复或向管理员发出警报。

3. 容错设计：考虑实施容错机制，使系统能够在错误发生时继续运行，而不至于完全崩溃。

这可能包括备份系统、冗余设计、错误恢复等。

4. 实时性要求：确保错误处理机制本身不会引入不可接受的延迟。

在实时系统中，处理错误的方式也需要考虑对系统性能的影响。

5. 自动恢复：实现自动恢复机制，使系统能够在发生错误后自动尝试修复问题，以减少对系统的干扰。

6. 监控和调试：在实时系统中加入监控和调试工具，以便能够实时监测系统的运行状态，迅速发现和诊断错误。

7. 测试和验证：在开发和部署过程中进行充分的测试，以确保系统在面临各种情况下都能正确处理错误。

这可能包括单元测试、集成测试和系统级测试。

总的来说，在处理实时错误时，关键是综合考虑系统的实时性要求、容错能力、自动恢复机制以及及时的监控和报警。

错误处理策略应该是系统设计和开发的一个重要组成部分。

数据库服务错误error26解决方法

错误提示：在建立与服务器的连接时出错。

在连接到SQL Server 2005 时，在默认的设置下SQL Server 不允许进行远程连接可能会导致此失败。

(provider: SQL 网络接口, error: 26 - 定位指定的服务器/实例时出错)有段时间遇到上面问题，在网上查找了许多方案，但那些方案都没能够解决问题，在解决其他问题时，发现一些其他端倪，返回来解决此问题，将该问题解决。

总结了我的解决方案（即下文中的方案一），并且我把在网上搜索来的解决方案也汇总了一下，与大家分享。

分析：“在建立与服务器的连接时出错”说明与数据库服务器有关；“在默认的设置下SQL Server 不允许进行远程连接可能会导致此失败。

”说明与权限有关；“provider: SQL 网络接口”说明当前登录身份是网络接口；“error: 26 - 定位指定的服务器/实例时出错”说明定位不上指定的服务器实例。

方案一：1、查看数据库服务是否存在；2、右击数据库实例服务--属性—登陆，将登陆身份改为本地系统（local system）；3、启动服务；4、若启动、暂停、停止等选项都是灰色，请查看启动类型是不是“禁用”了，若“禁用”了，请改为“自动”，然后启动服务；方案二：首先请确认SQL SERVER EXPRSS已安装到本地计算机上，并且SQL SERVER (SQLEXPRESS)服务已经成功启动。

如果问题仍然存在，可以尝试开始->所有程序->MS SQL Server2005->配置工具->MS SQL Server2005外围应用配置器-> “服务和连接的外围应用配置器”，然后将“远程连接”配置为“同时使用TCP/IP 和named pipes”。

Analysis Services下远程连接选择"本地连接和远程连接"，应用后重启。

由于启动用户实例的进程时出错，导致无法生成SQL Server 的用户实例。

errorhtml方法

errorhtml方法
errorhtml方法是一种在web开发中常用的方法，它用于在处理请求时发生错误时，返回一个HTML错误页面给客户端。

在使用errorhtml方法时，开发人员可以自定义错误页面的样式和内容，以便更好地展示错误信息给用户。

这样可以提升用户体验，并帮助用户更快地找到并解决问题。

要使用errorhtml方法，开发人员需要在代码中设置一个错误处理程序，并在该处理程序中调用errorhtml方法，将错误信息传递给客户端。

在处理请求时发生错误时，服务器会自动调用错误处理程序，并将错误信息传递给errorhtml方法。

然后，errorhtml方法会将错误信息填充到指定的HTML模板中，并将最终的HTML页面返回给客户端。

总的来说，errorhtml方法是一种非常常用的web开发方法，它可以帮助开发人员更好地处理错误，并提升用户体验。

- 1 -。

error

error

Sqldbcode=[ 10005 ]
SqlErrText=[ Select error: DBPROCESS 处于不可用或未启用状态。 ]
----------------------------------------
出错时间：2012-05-15 16:29
Sqldbcode=[ 10005 ]
----------------------------------------
出错时间：2012-03-13 16:09
Sqldbcode=[ 10005 ]
SqlErrText=[ Select error: DBPROCESS 处于不可用或未启用状态。 ]
----------------------------------------
出错时间：2012-05-14 20:26
Sqldbcode=[ 10025 ]
SqlErrText=[ Select error: 可能发生网络错误: 写入 SQL Server 失败。常规网络错误。请查阅文档。 ]
----------------------------------------
出错时间：2012-03-15 16:06
Sqldbcode=[ 10005 ]
SqlErrText=[ Select error: DBPROCESS 处于不可用或未启用状态。 ]
----------------------------------------
出错时间：2012-03-16 10:47
Sqldbcode=[ 107 ]
SqlErrText=[ Select error: 列前缀 'FYK' 与查询中所用的表名或别名不匹配。 ]

error

ORA-10561: block type 'TRANSACTION MANAGED DATA BLOCK', data object# 2427585
Incident details in: /opt/oracle/diag/rdbms/bislave2/orcl/incident/incdir_60292/orcl_pr07_20156_i60292.trc
TNS for Linux: Version 11.2.0.4.0 - Production
Oracle Bequeath NT Protocol Adapter for Linux: Version 11.2.0.4.0 - Production
TCP/IP NT Protocol Adapter for Linux: Version 11.2.0.4.0 - Production
See Note 411.1 at My Oracle Support for error and packaging details.
Recovery Slave PR07 previously exited with exception 600
MRP0: Background Media Recovery terminated with error 448
ORA-10567: Redo is inconsistent with data block (file# 25, block# 2092903, file offset is 4260159488 bytes)
ORA-10564: tablespace TBS_UA_DM
ORA-01110: data file 25: '/opt/oracle/oradata/orcl/UA_DM_DATA3.dbf'

ansys分析中遇到的error

ANSYS错误小结1、把体用面分割的时候出现的错误提示：Boolean operation failed.try adjusting the tolerance value on the BTOL commmand to some fraction of the minimum keypoint distance.Model Size (current problem)1.183933e+000,BTOL setting 1.00000e-005,minmum KP T distance 4.308365e-006先在要分割的地方设置一个工作平面，用布尔运算“divided --volume by working plane”进行分割的时候，出现上述错误，主要原因可能是设置的公差太小，当时试了几次都么有成功，最后干脆把体重新建立了一个，又画了一个很大的面，终于成功了。

2、一个常见的代表性错误！原来我的虚拟内存设置为“无分页文件”，现在改为“系统管理”，就不在出现计算内存不够的情况了。

Error!Element type 1 is Solid95,which can not be used with the AMES command, meshi ng of area 2 aborted.刚开始学习的人经常出这种错误，这是因为不同单元类型对应不同的划分网格操作。

上面的错误是说单元类型为Solid95（实体类型），不能用AMES命令划分面网格。

3、Meshing of volume 5 has been aborted because of a lack of memory. Closed d own other processes and/or choose a larger element size, then try the VMESH co mmand again. Minimum additional memory required=853MB（by kitty_zoe ）说你的内存空间不够，可能因为你的计算单元太多，增加mesh尺寸，减少数量或者增加最小内存设定（ansys10中在customization preferences菜单存储栏可以修改）你划分的网格太细了，内存不足。

电脑上网打开网页弹出error522错误代码怎么办？

电脑上⽹打开⽹页弹出error522错误代码怎么办？
电脑上⽹过程中打开⼀些⽹页会出现Error 522错误的提⽰，导致⽹页没办法正常使⽤，怎么办?Error 522错误是⽹站使⽤了百度云加速功能，但是加速CDN节点⽆法连接到源服务器，或是发⽣了超时导致的。

不要担⼼，对于访问⽤户来说没有办法，只能等待过会再试。

或是联系⽹站管理员检查。

解决⽅法：
站长检查：
1、源服务器是否正常，如服务器死机、卡死，IIS服务不正常等，可以通过hosts绑定源站IP的⽅式进⾏测试。

2、源站到节点⽹络是否稳定，可以通过在源站服务器上ping节点IP查询是否存在超时现象。

3、检查源站服务器/机房是否有软硬件防⽕墙对云加速的IP发起的请求进⾏了屏蔽。

4、如果源服务器访问正常，暂时停⽌百度云加速，并提交⼯单解决。

如源服务器或机房有防⽕墙，请将云加速的官⽅IP⽹段加⼊到您防⽕墙的⽩名单。

轻松解决上⽹打开⽹页出现Error 522错误的问题。

c语言error用法 -回复

c语言error用法-回复C语言Error 用法在C语言编程中，错误处理是一个非常重要且常见的主题。

当程序发生错误时，为了确保程序能够继续运行，我们需要使用错误处理机制。

C语言提供了一些内置的错误处理机制，其中一个重要的机制是通过使用错误码来标识程序中的错误。

1. 错误码的基本概念错误码是一个整数类型的值，用于标识在程序执行过程中发生的错误。

一个错误码通常包含两个方面的信息：错误的类型和错误的具体原因。

C 语言提供了`errno`这个全局变量来保存最近一次发生的错误码。

`errno`的值通常是一个负数，且在标准C库函数中被设置。

可以通过`#include <errno.h>`来包含这个头文件。

2. 错误返回值的用途当一个函数执行失败时，通常会返回一个错误码来指示错误的具体原因。

通过检查返回值，我们可以在程序的后续部分决定如何处理错误。

在C语言中，一个常见的用法是将错误码与一些预定义的常量进行比较，以便更好地理解错误的类型。

3. 错误码的预定义常量C语言提供了一些预定义的常量来表示常见的错误类型。

这些常量通常以`E` 开头，后面紧跟一个大写字母或数字，例如`EACCES` 表示"Permission denied"。

这些常量定义在`errno.h` 头文件中，它们提供了一个易于记忆和跨平台的标准错误码集。

4. 错误处理函数的使用C语言提供了一些用于处理错误的函数，其中一个常用的函数是`perror()`。

这个函数可以根据当前的错误码，在标准错误输出流上输出与`errno` 相关的错误描述信息。

例如，我们可以使用以下代码来输出错误信息:#include <stdio.h>#include <errno.h>int main() {FILE *file = fopen("example.txt", "r");if (file == NULL) {perror("Error");return errno;}fclose(file);return 0;}在上面的代码片段中，我们尝试打开一个不存在的文件。

当遇到error：stray241inprogram错误的解决方法

代码中出现了中文空格中文引号各种中文标点符号都会出现简单修改一下就ok了
当遇到 error： stray241inprogram错误的解决方法当遇到 error: stray '\241' in 错误的解决方法
报错的意思是c/c++中的产生了编译错误。该错误是指源程序中有非法字符，需要将非法字符去掉。一般是由于coder使用中文输入法或者从别的地方直接复制粘贴代码造成的。代码中出现了中文空格，中文引号，各种中文标点符号都会出现，简单修改一下就OK了。
解决方法： 1.把出错行的空格及其前后空格删掉重新打一下试试。 2.把明显和其他标点符号颜色不同的改掉。（大部分编译器都有颜色识别） 3.直接全部重打。

indexerror的意思

indexerror的意思
IndexError 是 Python 中的一个错误类型，它表示当尝试访问一个不存在的列表元素时发生的错误。

例如，如果您尝试访问列表中未定义的元素，则会发生 IndexError 错误。

IndexError 错误通常发生在用户尝试访问列表中的未知索引时。

例如，如果您尝试访问列表中的第二个元素，但列表只包含一个元素，则会发生IndexError 错误。

此外，如果您尝试访问列表的未定义索引，例如尝试访问列表中的第二个元素，但列表只包含一个元素，则也会发生 IndexError 错误。

为了避免发生 IndexError 错误，您可以在编写代码时检查索引是否在列表范围内。

例如，您可以使用 if 语句来检查索引是否在列表范围内，并在必要时抛出错误。

例如:
```python
my_list = [1, 2, 3, 4, 5]
if my_list[2] != 4:
raise IndexError("索引错误：列表中不存在索引 2")
print(my_list[2]) # 输出:4
```
在上面的示例中，如果您尝试访问 my_list[2] 索引，则会抛出 IndexError 错误，因为 my_list 只包含四个元素，不存在索引 2。

总结起来，IndexError 错误通常是由于用户尝试访问未定义的列表元素引起的。

为了防止此类错误，您可以在编写代码时仔细检查索引是否在列表范围内，并在必要时抛出错误。

mistake error ,slip 等大的区别

Mistake /error/slip等的区别Slip a small mistake,usually made by being careless or not paying attention 差错，纰漏，疏漏He recited the whole poem without making a single slipMistake a word or a figure that is not said or written down correctly 指用词或数字上的错误、口误、笔误It’s a common mistake among learners of English. Spelling mistakesError(rather formal) a word or a figure ,etc.that is not said or written down correctly 指用词、数字等错误、口误、笔误There are two many errors in your work. 你的工作失误太多。

Inaccuracy (rather formal) a piece of information that is not exactly correct指信息不准确、有误The article is full of inaccuracies . 这篇文章里不准确的地方比比皆是。

Howler(informal,especially BrE) a stupid mistake ,especially in what sb says or writes 尤指言谈或行文中的愚蠢错误；The report is full of howlers.(A howler is usually an embarrassing does not know sth that they really should know.howler 通常指令人难堪的错误，表明犯错误者不知道应该知道的东西）Misprint a small mistake in printed text 纸印刷文本上的错误Typo (informal)a small mistake in typed or printed text 指打字或排印文稿的小错误（Typo 多为校对人员校勘书籍、杂志等印刷错误时使用。

Error

He is works in a bank.
3.Double markings
（双重标记）
The use of a tense marker twice in one sentence. They didn’t played basketball yesterday.
4.Misformations
What you are doing?
Why she is crying?
They were danced in the party last night.
这说明学习者对过去式的理解还不是很充分。
2. In terms of the nature of errors
Type 1.Omissions （省略） 2.Additions （添加） Explanation The absence of an item that must appear in a well formed utterance. The presence of an item that must not appear in well-formed utterance. She running. Example
Mistake
1.learners have already 2.learned the knowledge or skill 3.fail to function correctly due to lack of attention or other factors.
Classification of errors
（形式错误）
The use of the wrong form of the morpheme or structure.
The boy finished to do his homework.

internalservererror原因及解决

internalservererror原因及解决
常见的内部服务错误的原因有⼆，⼀是服务器资源紧张，⼆是⽂件权限错误。

1.错误的原因⼀：服务器资源超载。

服务器的资源超载：即同⼀时间内处理器有太多的进程需要处理的时候，会出现500错误。

借助SSH，可以在命令⾏中输⼊以下命令查看：ps aux |grep username如果查到某个进程消耗过多资源，可以⽤kill命令强制关闭这个进程，只需输⼊该进程的进程号(Pid)：kill -9 pid。

2.错误的原因⼆：⽂件权限设置错误。

500错误还有可能是对⽂件设置了不正确的权限：后台⽬录和⽂件的权限默认应该是755，⽽图⽚，⽂字等html⽂件应该是644，所以如果出现500错误，应该主要检查⽂件权限设置。

可以使⽤FTP软件选中所有⽂件，然后批量修改⽂件权限。

error是什么意思

error是什么意思我们常常会看到电脑出现error这个单词，但是error又是什么意思呢?下面是小编整理的error的意思，大家快来看看吧error的意思：n.错误;过失;误差;谬误error的英语音标：英 ['erə(r)] 美 ['erər]error的英语例句：He overlooked a spelling error on the first page.他没有看出第一页中有个拼写错误。

An alert listener will have noticed the error.耳朵尖的人能听出这个错。

Every man is liable to error.人人都可能犯错误。

The accident was caused by human error.这宗事故是人为过失造成的。

Human error invoked the disaster.人的过失带来灾难。

Error analysis of the measurement system is given.给出了次测量系统的误差分析。

After several repetitions there still remains an error.重复数次之后仍然存在误差。

Divination is made up of a little error and superstition, plus a lot of fraud.占卜是由一些谬误和迷信构成，再加上大量的欺骗。

The plane was shot down in error by a NATO missile 一枚北约的导弹误将那架飞机击落。

The hospital blamed the mix-up on a clerical error 医院方面将这一混乱归咎于一处笔误。

You have to allow for a certain amount of error 你必须将一定量的误差考虑在内。