A Neural Network Approach to Simulate Biodiesel Production from

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外文翻译---人工神经网络

外文翻译---人工神经网络

英文文献英文资料:Artificial neural networks (ANNs) to ArtificialNeuralNetworks, abbreviations also referred to as the neural network (NNs) or called connection model (ConnectionistModel), it is a kind of model animals neural network behavior characteristic, distributed parallel information processing algorithm mathematical model. This network rely on the complexity of the system, through the adjustment of mutual connection between nodes internal relations, so as to achieve the purpose of processing information. Artificial neural network has since learning and adaptive ability, can provide in advance of a batch of through mutual correspond of the input/output data, analyze master the law of potential between, according to the final rule, with a new input data to calculate, this study analyzed the output of the process is called the "training". Artificial neural network is made of a number of nonlinear interconnected processing unit, adaptive information processing system. It is in the modern neuroscience research results is proposed on the basis of, trying to simulate brain neural network processing, memory information way information processing. Artificial neural network has four basic characteristics:(1) the nonlinear relationship is the nature of the nonlinear common characteristics. The wisdom of the brain is a kind of non-linear phenomena. Artificial neurons in the activation or inhibit the two different state, this kind of behavior in mathematics performance for a nonlinear relationship. Has the threshold of neurons in the network formed by the has better properties, can improve the fault tolerance and storage capacity.(2) the limitations a neural network by DuoGe neurons widely usually connected to. A system of the overall behavior depends not only on the characteristics of single neurons, and may mainly by the unit the interaction between the, connected to the. Through a large number of connection between units simulation of the brain limitations. Associative memory is a typical example of limitations.(3) very qualitative artificial neural network is adaptive, self-organizing, learning ability. Neural network not only handling information can have all sorts of change, and in the treatment of the information at the same time, the nonlinear dynamic system itself is changing. Often by iterative process description of the power system evolution.(4) the convexity a system evolution direction, in certain conditions will depend on a particular state function. For example energy function, it is corresponding to the extreme value of the system stable state. The convexity refers to the function extreme value, it has DuoGe DuoGe system has a stable equilibrium state, this will cause the system to the diversity of evolution.Artificial neural network, the unit can mean different neurons process of the object, such as characteristics, letters, concept, or some meaningful abstract model. The type of network processing unit is divided into three categories: input unit, output unit and hidden units. Input unit accept outside the world of signal and data; Output unit of output system processing results; Hidden unit is in input and output unit, not between by external observation unit. The system The connections between neurons right value reflect the connection between the unit strength, information processing and embodied in the network said the processing unit in the connections. Artificial neural network is a kind of the procedures, and adaptability, brain style of information processing, its essence is through the network of transformation and dynamic behaviors have akind of parallel distributed information processing function, and in different levels and imitate people cranial nerve system level of information processing function. It is involved in neuroscience, thinking science, artificial intelligence, computer science, etc DuoGe field cross discipline.Artificial neural network is used the parallel distributed system, with the traditional artificial intelligence and information processing technology completely different mechanism, overcome traditional based on logic of the symbols of the artificial intelligence in the processing of intuition and unstructured information of defects, with the adaptive, self-organization and real-time characteristic of the study.Development historyIn 1943, psychologists W.S.M cCulloch and mathematical logic W.P home its established the neural network and the math model, called MP model. They put forward by MP model of the neuron network structure and formal mathematical description method, and prove the individual neurons can perform the logic function, so as to create artificial neural network research era. In 1949, the psychologist put forward the idea of synaptic contact strength variable. In the s, the artificial neural network to further development, a more perfect neural network model was put forward, including perceptron and adaptive linear elements etc. M.M insky, analyzed carefully to Perceptron as a representative of the neural network system function and limitations in 1969 after the publication of the book "Perceptron, and points out that the sensor can't solve problems high order predicate. Their arguments greatly influenced the research into the neural network, and at that time serial computer and the achievement of the artificial intelligence, covering up development new computer and new ways of artificial intelligence and the necessity and urgency, make artificial neural network of research at a low. During this time, some of the artificial neural network of the researchers remains committed to this study, presented to meet resonance theory (ART nets), self-organizing mapping, cognitive machine network, but the neural network theory study mathematics. The research for neural network of research and development has laid a foundation. In 1982, the California institute of J.J.H physicists opfield Hopfield neural grid model proposed, and introduces "calculation energy" concept, gives the network stability judgment. In 1984, he again put forward the continuous time Hopfield neural network model for the neural computers, the study of the pioneering work, creating a neural network for associative memory and optimization calculation, the new way of a powerful impetus to the research into the neural network, in 1985, and scholars have proposed a wave ears, the study boltzmann model using statistical thermodynamics simulated annealing technology, guaranteed that the whole system tends to the stability of the points. In 1986 the cognitive microstructure study, puts forward the parallel distributed processing theory. Artificial neural network of research by each developed country, the congress of the United States to the attention of the resolution will be on jan. 5, 1990 started ten years as the decade of the brain, the international research organization called on its members will the decade of the brain into global behavior. In Japan's "real world computing (springboks claiming)" project, artificial intelligence research into an important component.Network modelArtificial neural network model of the main consideration network connection topological structure, the characteristics, the learning rule neurons. At present, nearly 40 kinds of neural network model, with back propagation network, sensor, self-organizing mapping, the Hopfieldnetwork.the computer, wave boltzmann machine, adapt to the ear resonance theory. According to the topology of the connection, the neural network model can be divided into:(1) prior to the network before each neuron accept input and output level to the next level, the network without feedback, can use a loop to no graph. This network realization from the input space to the output signal of the space transformation, it information processing power comes from simple nonlinear function of DuoCi compound. The network structure is simple, easy to realize. Against the network is a kind of typical prior to the network.(2) the feedback network between neurons in the network has feedback, can use a no to complete the graph. This neural network information processing is state of transformations, can use the dynamics system theory processing. The stability of the system with associative memory function has close relationship. The Hopfield network.the computer, wave ear boltzmann machine all belong to this type.Learning typeNeural network learning is an important content, it is through the adaptability of the realization of learning. According to the change of environment, adjust to weights, improve the behavior of the system. The proposed by the Hebb Hebb learning rules for neural network learning algorithm to lay the foundation. Hebb rules say that learning process finally happened between neurons in the synapse, the contact strength synapses parts with before and after the activity and synaptic neuron changes. Based on this, people put forward various learning rules and algorithm, in order to adapt to the needs of different network model. Effective learning algorithm, and makes the godThe network can through the weights between adjustment, the structure of the objective world, said the formation of inner characteristics of information processing method, information storage and processing reflected in the network connection. According to the learning environment is different, the study method of the neural network can be divided into learning supervision and unsupervised learning. In the supervision and study, will the training sample data added to the network input, and the corresponding expected output and network output, in comparison to get error signal control value connection strength adjustment, the DuoCi after training to a certain convergence weights. While the sample conditions change, the study can modify weights to adapt to the new environment. Use of neural network learning supervision model is the network, the sensor etc. The learning supervision, in a given sample, in the environment of the network directly, learning and working stages become one. At this time, the change of the rules of learning to obey the weights between evolution equation of. Unsupervised learning the most simple example is Hebb learning rules. Competition rules is a learning more complex than learning supervision example, it is according to established clustering on weights adjustment. Self-organizing mapping, adapt to the resonance theory is the network and competitive learning about the typical model.Analysis methodStudy of the neural network nonlinear dynamic properties, mainly USES the dynamics system theory and nonlinear programming theory and statistical theory to analysis of the evolution process of the neural network and the nature of the attractor, explore the synergy of neural network behavior and collective computing functions, understand neural information processing mechanism. In order to discuss the neural network and fuzzy comprehensive deal of information may, the concept of chaos theory and method will play a role. The chaos is a rather difficult toprecise definition of the math concepts. In general, "chaos" it is to point to by the dynamic system of equations describe deterministic performance of the uncertain behavior, or call it sure the randomness. "Authenticity" because it by the intrinsic reason and not outside noise or interference produced, and "random" refers to the irregular, unpredictable behavior, can only use statistics method description. Chaotic dynamics of the main features of the system is the state of the sensitive dependence on the initial conditions, the chaos reflected its inherent randomness. Chaos theory is to point to describe the nonlinear dynamic behavior with chaos theory, the system of basic concept, methods, it dynamics system complex behavior understanding for his own with the outside world and for material, energy and information exchange process of the internal structure of behavior, not foreign and accidental behavior, chaos is a stationary. Chaotic dynamics system of stationary including: still, stable quantity, the periodicity, with sex and chaos of accurate solution... Chaos rail line is overall stability and local unstable combination of results, call it strange attractor.A strange attractor has the following features: (1) some strange attractor is a attractor, but it is not a fixed point, also not periodic solution; (2) strange attractor is indivisible, and that is not divided into two and two or more to attract children. (3) it to the initial value is very sensitive, different initial value can lead to very different behavior.superiorityThe artificial neural network of characteristics and advantages, mainly in three aspects: first, self-learning. For example, only to realize image recognition that the many different image model and the corresponding should be the result of identification input artificial neural network, the network will through the self-learning function, slowly to learn to distinguish similar images. The self-learning function for the forecast has special meaning. The prospect of artificial neural network computer will provide mankind economic forecasts, market forecast, benefit forecast, the application outlook is very great. The second, with lenovo storage function. With the artificial neural network of feedback network can implement this association. Third, with high-speed looking for the optimal solution ability. Looking for a complex problem of the optimal solution, often require a lot of calculation, the use of a problem in some of the design of feedback type and artificial neural network, use the computer high-speed operation ability, may soon find the optimal solution.Research directionThe research into the neural network can be divided into the theory research and application of the two aspects of research. Theory study can be divided into the following two categories:1, neural physiological and cognitive science research on human thinking and intelligent mechanism.2, by using the neural basis theory of research results, with mathematical method to explore more functional perfect, performance more superior neural network model, the thorough research network algorithm and performance, such as: stability and convergence, fault tolerance, robustness, etc.; The development of new network mathematical theory, such as: neural network dynamics, nonlinear neural field, etc.Application study can be divided into the following two categories:1, neural network software simulation and hardware realization of research.2, the neural network in various applications in the field of research. These areas include: pattern recognition, signal processing, knowledge engineering, expert system, optimize the combination, robot control, etc. Along with the neural network theory itself and related theory, related to the development of technology, the application of neural network will further.Development trend and research hot spotArtificial neural network characteristic of nonlinear adaptive information processing power, overcome traditional artificial intelligence method for intuitive, such as mode, speech recognition, unstructured information processing of the defects in the nerve of expert system, pattern recognition and intelligent control, combinatorial optimization, and forecast areas to be successful application. Artificial neural network and other traditional method unifies, will promote the artificial intelligence and information processing technology development. In recent years, the artificial neural network is on the path of human cognitive simulation further development, and fuzzy system, genetic algorithm, evolution mechanism combined to form a computational intelligence, artificial intelligence is an important direction in practical application, will be developed. Information geometry will used in artificial neural network of research, to the study of the theory of the artificial neural network opens a new way. The development of the study neural computers soon, existing product to enter the market. With electronics neural computers for the development of artificial neural network to provide good conditions.Neural network in many fields has got a very good application, but the need to research is a lot. Among them, are distributed storage, parallel processing, since learning, the organization and nonlinear mapping the advantages of neural network and other technology and the integration of it follows that the hybrid method and hybrid systems, has become a hotspot. Since the other way have their respective advantages, so will the neural network with other method, and the combination of strong points, and then can get better application effect. At present this in a neural network and fuzzy logic, expert system, genetic algorithm, wavelet analysis, chaos, the rough set theory, fractal theory, theory of evidence and grey system and fusion.汉语翻译人工神经网络(ArtificialNeuralNetworks,简写为ANNs)也简称为神经网络(NNs)或称作连接模型(ConnectionistModel),它是一种模范动物神经网络行为特征,进行分布式并行信息处理的算法数学模型。

人工智能原理_北京大学中国大学mooc课后章节答案期末考试题库2023年

人工智能原理_北京大学中国大学mooc课后章节答案期末考试题库2023年

人工智能原理_北京大学中国大学mooc课后章节答案期末考试题库2023年1.Turing Test is designed to provide what kind of satisfactory operationaldefinition?图灵测试旨在给予哪一种令人满意的操作定义?答案:machine intelligence 机器智能2.Thinking the differences between agent functions and agent programs, selectcorrect statements from following ones.考虑智能体函数与智能体程序的差异,从下列陈述中选择正确的答案。

答案:An agent program implements an agent function.一个智能体程序实现一个智能体函数。

3.There are two main kinds of formulation for 8-queens problem. Which of thefollowing one is the formulation that starts with all 8 queens on the boardand moves them around?有两种8皇后问题的形式化方式。

“初始时8个皇后都放在棋盘上,然后再进行移动”属于哪一种形式化方式?答案:Complete-state formulation 全态形式化4.What kind of knowledge will be used to describe how a problem is solved?哪种知识可用于描述如何求解问题?答案:Procedural knowledge 过程性知识5.Which of the following is used to discover general facts from trainingexamples?下列中哪个用于训练样本中发现一般的事实?答案:Inductive learning 归纳学习6.Which statement best describes the task of “classification” in machinelearning?哪一个是机器学习中“分类”任务的正确描述?答案:To assign a category to each item. 为每个项目分配一个类别。

人工神经网络综述论文

人工神经网络综述论文

人工神经网络的最新发展综述摘要:人工神经网络是指模拟人脑神经系统的结构和功能,运用大量的处理部件,由人工方式建立起来的网络系统。

该文首先介绍了神经网络研究动向,然后介绍了近年来几种新型神经网络的基本模型及典型应用,包括模糊神经网络、神经网络与遗传算法的结合、进化神经网络、混沌神经网络和神经网络与小波分析的结合。

最后,根据这几种新型神经网络的特点,展望了它们今后的发展前景。

关键词:模糊神经网络;神经网络与遗传算法的结合;进化神经网络;混沌神经网络;神经网络与小波分析。

The review of the latest developments in artificial neuralnetworksAbstract:Artificial neural network is the system that simulates the human brain’s structure and function, and uses a large number of processing elements, and is manually established by the network system. This paper firstly introduces the research trends of the neural network, and then introduces several new basic models of neural networks and typical applications in recent years, including of fuzzy neural network, the combine of neural network and genetic algorithm, evolutionary neural networks, chaotic neural networks and the combine of neural networks and wavelet analysis. Finally, their future prospects are predicted based on the characteristics of these new neural networks in the paper.Key words: Fuzzy neural network; Neural network and genetic algorithm; Evolutionary neural networks; Chaotic neural networks; Neural networks and wavelet analysis1 引言人工神经网络的研究始于20世纪40年代初。

模拟ai英文面试题目及答案

模拟ai英文面试题目及答案

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

基于NARX神经网络的磁悬浮仿真模型(IJISA-V5-N5-4)

基于NARX神经网络的磁悬浮仿真模型(IJISA-V5-N5-4)

II. Mathematical Model of Magnetic Levitation System Main parts of laboratory magnetic levitation system are electromagnetic coil, position sensor, steel ball and steel frame that can be divided into three parts. On the top of the frame the electromagnetic coil iig. 1: Magnetic levitation system
d2x i mg K f dt 2 x .
Simulation Model of Magnetic Levitation Based on NARX Neural Networks
Dragan Antić, Miroslav Milovanović, Saša Nikolić, Marko Milojković, Staniša Perić University of Niš, Faculty of Electronic Engineering, Department of Control Systems, Aleksandra Medvedeva 14, 18000 Niš, Republic of Serbia, Phone: (+381) 18529363, Fax: (+381) 18588399 E-mails: {dragan.antic, ovanovic2, sasa.s.nikolic, ojkovic, stanisa.peric}@elfak.ni.ac.rs Abstract — In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX) of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details. Index Terms — Neural Network, Magnetic Levitation System, Nonlinear Model, Neural Network Training The similar approach was given in [6], where neural network with built-in nominal linear model is shown. Linear model is provided by setting some network weights to desired and in advance defined values. Other weights are free at start and they are changing their values during learning process. Initial stability is provided by the linear model control at the start of the training process. The main objective of this paper is to simulate working process of magnetic levitation system by implementing NARX model of neural network using MATLAB software. NARX model represents nonlinear autoregressive network with external inputs, and it is based on linear autoregressive model. NARX represents recurrent dynamical feedback network with predictive structure. Main goal of this predictive model is to predict future system behaviour for known and unknown input vectors. Model prediction accuracy directly determines quality and efficiency of the control law. Primary consideration of work accuracy during implementation process is of a great importance. The paper is organized as follows. In Section II, the mathematical model of magnetic levitation system is given. The structure of neural network based on NARX model is presented in Section III. In next Section, different training types of neural network are described. The experimental results are illustrated and discussed in Section V. It is shown that the best simulation performances are obtained using Basic Quasi–Newton and Backpropagation method. The concluding remarks are given in Section VI.

仁爱英语八下u8t3复习课件

仁爱英语八下u8t3复习课件

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Intelligent agent: A system that perceives its environment and takes actions that maximizes its chances of success
Machine learning: A subset of artificial intelligence that enables computers to learn from data without being explicitly programmed
Interactive listening activities
with build in quizzes and comprehension checks to engage students and assess their understanding
Adjustable playback speed
Peer and teacher feedback
through collaborative discussions and one on one conferences, students receive constructive criticism and resource to improve their listening comprehension
capable of
abstractions in data
recognizing patterns
in data
Natural language processing (NLP): A field of AI that deals with the interactions between computers and human languages

人工智能原理MOOC习题集及答案 北京大学 王文敏

人工智能原理MOOC习题集及答案 北京大学 王文敏

Quizzes for Chapter 11单选(1分)图灵测试旨在给予哪一种令人满意的操作定义得分/总分∙ A.人类思考∙ B.人工智能∙ C.机器智能1.00/1.00 ∙D.机器动作正确答案:C 你选对了2多选(1分)选择以下关于人工智能概念的正确表述得分/总分∙ A.人工智能旨在创造智能机器该题无法得分/1.00 ∙B.人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/1.00∙C.人工智能将其定义为人类智能体的研究该题无法得分/1.00∙ D.人工智能是为了开发一类计算机使之能够完成通常由人类所能做的事该题无法得分/1.00 正确答案:A 、B 、D你错选为A 、B 、C 、D3多选(1分)如下学科哪些是人工智能的基础?得分/总分∙ A.经济学0.25/1.00 ∙ B.哲学0.25/1.00∙ C.心理学0.25/1.00∙D.数学0.25/1.00正确答案:A 、B 、C 、D 你选对了4多选(1分)下列陈述中哪些是描述强AI (通用AI )的正确答案?得分/总分∙ A.指的是一种机器,具有将智能应用于任何问题的能力0.50/1.00∙ B.是经过适当编程的具有正确输入和输出的计算机,因此有与人类同样判断力的头脑0.50/1.00 ∙C.指的是一种机器,仅针对一个具体问题 ∙D.其定义为无知觉的计算机智能,或专注于一个狭窄任务的AI正确答案:A 、B 你选对了5多选(1分)选择下列计算机系统中属于人工智能的实例得分/总分∙ A.Web 搜索引擎 ∙ B.超市条形码扫描器∙ C.声控电话菜单该题无法得分/1.00 ∙D.智能个人助理该题无法得分/1.00正确答案:A 、D 你错选为C 、D6多选(1分)选择下列哪些是人工智能的研究领域 得分/总分∙ A.人脸识别0.33/1.00 ∙B.专家系统0.33/1.00 ∙C.图像理解 ∙D.分布式计算正确答案:A 、B 、C 你错选为A 、B7多选(1分)考察人工智能(AI)的一些应用,去发现目前下列哪些任务可以通过AI 来解决得分/总分∙A.以竞技水平玩德州扑克游戏0.33/1.00 ∙B.打一场像样的乒乓球比赛∙ C.在Web 上购买一周的食品杂货0.33/1.00 ∙D.在市场上购买一周的食品杂货正确答案:A 、B 、C 你错选为A 、C8填空(1分)理性指的是一个系统的属性,即在_________的环境下做正确的事。

基于仿生眼的无人机视觉跟踪云台摄像机控制系统(英文)

基于仿生眼的无人机视觉跟踪云台摄像机控制系统(英文)

In the UAV visual tracking system, the UAV, the onboard camera and the ground target are all in motion. Therefore, the system has the following characteristics. Firstly, UAV is inherently unstable and capable of exhibiting high acceleration rates, in addition there are the engine vibration and the wind, thus the image from the onboard camera is very unstable. Moreover, the image can't be processed using normal spatio-temporal filtering of the camera image sequence for estimation of local motion. Next, the system demands high-performance real-time processing. But UAV has limited on-board power and payload capacity, so that it limits the usage of the on-board hardware. Thus, the new appropriate hardware must be designed; meanwhile, the adaptive robust algorithm must be introduced. The vision and the control system must be compact, efficient, and lightweight for effective on-board integration. Thirdly, the on-board camera is always in motion, the distance and direction between the on-board camera and ground moving target change constantly, so the existing target recognition and tracking method, which fit for the image from the static camera, is no longer suitable. It is insufficient to only control the camera for UAV visual tracking system. The rolling angle and pitching angle of the on-board camera are requested to be extremely accurate. Once the angle has a tiny deflection, the target may be lost. Moreover, the flight attitude seriously influences the view of the on-board camera. The stable tracking must coordinate to ontrol the flight attitude of UAV and the movement of the onboard camera.

《人工智能英语》试卷(含答案)

《人工智能英语》试卷(含答案)

参考试卷一、写出以下单词的中文意思(每小题0.5分,共10分)1 accuracy 11 customize2 actuator 12 definition3 adjust 13 defuzzification4 agent 14 deployment5 algorithm 15 effector6 analogy 16 entity7 attribute 17 extract8 backtrack 18 feedback9 blockchain 19 finite10 cluster 20 framework二、根据给出的中文意思,写出英文单词(每小题0.5分,共10分)1 v.收集,搜集11 n.神经元;神经细胞2 adj.嵌入的,内置的12 n.节点3 n.指示器;指标13 v.运转;操作4 n.基础设施,基础架构14 n.模式5 v.合并;集成15 v.察觉,发觉6 n.解释器,解释程序16 n.前提7 n.迭代;循环17 adj.程序的;过程的8 n.库18 n.回归9n.元数据19 adj.健壮的,强健的;结实的10 v.监视;控制;监测20 v.筛选三、根据给出的短语,写出中文意思(每小题1分,共10分)1 data object2 cyber security3 smart manufacturing4 clustered system5 data visualization6 open source7 analyze text8 cloud computing9 computation power10 object recognition四、根据给出的中文意思,写出英文短语(每小题1分,共10分)1 数据结构2 决策树3 演绎推理4 贪婪最佳优先搜索5 隐藏模式,隐含模式6 知识挖掘7 逻辑推理8 预测性维护9 搜索引擎10 文本挖掘技术五、写出以下缩略语的完整形式和中文意思(每小题1分,共10分)缩略语完整形式中文意思1 ANN2 AR3 BFS4 CV5 DFS6 ES7 IA8 KNN9 NLP10 VR六、阅读短文,回答问题(每小题2分,共10分)Artificial Neural Network (ANN)An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain.An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs alsouse a set of learning rules called backpropagation, an abbreviation for backward propagation of error, to perfect their output results.An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce — the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backward, going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.A neural network may contain the following 3 layers:Input layer – The activity of the input units represents the raw information that can feed into the network.Hidden layer – To determine the activity of each hidden unit. The activities of the input units and the weights on the connections between the input and the hidden units. There may be one or more hidden layers.Output layer – The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.1. What is an artificial neural network (ANN)?2.What is each neuron made up of?3.Wha do the input units do?4.What does an ANN initially go through?5.How many layers may a neural network contain? What are they?七、将下列词填入适当的位置(每词只用一次)。

两类神经网络的cmos模拟电路设计与分析

两类神经网络的cmos模拟电路设计与分析

摘要神经网络是模拟人脑基本特性的智能系统,也是一门信息处理的科学。

神经网络具有自适应学习、非线性映射、分布并行处理等特点。

神经网络从单个神经元的模拟,到最终模拟大脑的信息处理功能。

神经网络应用非常广泛,目前主要运用于非线性系统、网络故障、航空航天、智能机器人等领域。

对于神经网络的研究主要分为三部分:理论研究、应用研究和实现技术研究。

而实现技术上,主要有两种实现方法:软件实现和硬件实现。

用软件实现神经网络,具有处理速度、并行程度低等缺点,这很难满足神经网络信息处理的实时性的要求。

用硬件实现神经网络能体现网络的快速性、并行计算,且能实现大规模的信号处理,这在复杂的数据处理场合中是非常有利的。

因此,硬件实现是神经网络发展的必然趋势。

硬件实现方法中,基于模拟CMOS电路实现神经网络电路具有结构简单、集成速度快、占用芯片面积小、集成度高、功耗低等特点,因此本文研究采用模拟CMOS集成电路设计神经网络。

神经网络模型中具有代表性的有:误差反向传播BP网络、径向基函数RBF 网络、自组织网络、感知器、反馈Hopfield网络、小脑模型CMAC网络、模糊神经网络等。

目前,已经用硬件实现了的神经网络有:BP网络、RBF网络、感知器等,而在其他的网络模型的硬件实现方案甚少。

基于研究神经网络的全面性,本文主要研究用模拟CMOS电路实现自组织竞争神经和模糊神经网络,围绕这两种神经网络,做了如下相关工作:(1)针对神经网络的神经元模型中权值不可调的缺点,设计了线性可调运算跨导放大器和电流乘法器电路作为突触电路,通过改变外部电流实现权值可调功能,且设计的电路结构简单,线性度高。

以此能作为基本单元应用于神经元电路中。

(2)基于自组织竞争神经网络中竞争层算法难以实现的问题,设计了一种电流模式的最值电路模拟实现竞争算法,通过比较电流的大小达到竞争目的。

该电路实现简单、模拟程度高、便于集成,与输入层结合能实现自组织竞争神经网络。

(3)针对模糊神经网络的单元电路结构复杂且精度低的问题,本文对高斯函数电路、求小电路、去模糊电路的结构进行优化设计,从整体上提高模糊神经网络的精度和高速性。

人工智能英语词汇

人工智能英语词汇

人工智能英语词汇1. Artificial Intelligence (AI) - the simulation of human intelligence in machines that are programmed to think and learn like humans.2. Machine Learning - a subset of AI that enables machines to learn and make predictions or decisions without being explicitly programmed.3. Deep Learning - a type of machine learning that uses artificial neural networks to simulate human-like thinking and learning patterns.4. Natural Language Processing (NLP) - the ability of a computer to understand and interpret human language in a useful way.5. Computer Vision - a field of AI that focuses on enabling computers to see, interpret, and understand visual information like humans.6. Robotics - the branch of technology that deals with the design, construction, and operation of robots, often integrating AI capabilities.7. Neural Networks - a complex network of algorithms inspired by the structure and function of the human brain, used in machine learning.8. Data Mining - the process of searching and analyzing large sets of data to discover patterns, trends, and insights.9. Speech Recognition - the technology that allows computers to recognize and interpret spoken language.10. Expert Systems - computer systems that are designed to mimic the decision-making abilities of a human expert in a specific domain.。

人工智能深度学习技术练习(习题卷13)

人工智能深度学习技术练习(习题卷13)

人工智能深度学习技术练习(习题卷13)第1部分:单项选择题,共47题,每题只有一个正确答案,多选或少选均不得分。

1.[单选题]已知- 大脑是有很多个叫做神经元的东西构成,神经网络是对大脑的简单的数学表达。

- 每一个神经元都有输入、处理函数和输出。

- 神经元组合起来形成了网络,可以拟合任何函数。

- 为了得到最佳的神经网络,我们用梯度下降方法不断更新模型给定上述关于神经网络的描述,什么情况下神经网络模型被称为深度学习模型?A)加入更多层,使神经网络的深度增加B)有维度更高的数据C)当这是一个图形识别的问题时D)以上都不正确答案:A解析:难易程度:易题型:2.[单选题]DataLoader中batch_size的作用是A)批次大小B)是否乱序C)使用多进程读取数据,设置的进程数。

D)是否丢弃最后一个样本数量不足batch_size批次数据。

答案:A解析:3.[单选题]关于Python的全局变量和局部变量,以下选项中描述错误的是()。

A)局部变量指在函数内部使用的变量,当函数退出时,变量依然存在,下次函数调用可以继续使用B)全局变量指在函数之外定义的变量,-般没有缩进,在程序执行全过程有效C)使用global保留字声明简单数据类型变量后,该变量作为全局变量使用D)简单数据类型变量无论是否与全局变量重名,仅在函数内部创建和使用,函数退出后变量被释放答案:A解析:难易程度:易题型:4.[单选题]本学期的大作业,手写数字识别性能的最低要求是多少()。

A)90%B)95%C)97%D)99%答案:B解析:难易程度:易题型:5.[单选题]将一个骰子的“2”修改成“1”,那么掷这个骰子得信息熵会。

A)增大B)减少C)不变D)不确定答案:B解析:答案:C解析:7.[单选题]()并不会改变网络,他会对神经元做随机删减,从而使得网络复杂度降低,有效的防止过拟合。

A)Batch NormalizationB)L1正则化C)L2正则化D)Dropout答案:D解析:Dropout并不会改变网络,他会对神经元做随机删减,从而使得网络复杂度降低,有效的防止过拟合。

人工智能会取代人脑吗英语作文

人工智能会取代人脑吗英语作文

人工智能会取代人脑吗英语作文The Future of Intelligence: AI vs. Human Brain.As the world rapidly evolves, so does the realm of technology, leading to advancements that challenge our understanding of what is possible. One such area that has garnered significant attention in recent years isartificial intelligence (AI). With its ability to process vast amounts of data and make decisions with remarkable speed and accuracy, AI has raised the question: will itever replace the human brain?To answer this question, we must first understand the fundamental differences between AI and the human brain. AIis a product of human ingenuity, a complex system designedto simulate certain cognitive functions. It is programmedto recognize patterns, learn from experiences, and make decisions based on the data it has been trained on. However, AI lacks the emotional intelligence, creativity, and subjectivity that defines human thought.The human brain, on the other hand, is a biological marvel. It is capable of not only rational thinking but also emotional responses, intuition, and creativity. The brain's neural networks are interconnected in a way that allows for unique experiences and individuality, making each human brain distinct. Furthermore, the brain has the ability to adapt and learn throughout life, acquiring new skills and knowledge.Despite these differences, AI has already made significant progress in certain areas, such as image recognition and language processing. Its speed and accuracy in these tasks have surpassed human capabilities. However, this does not mean that AI will replace the human brain across all functions. There are inherent limitations to AI that prevent it from fully replicating human intelligence.One such limitation is the lack of contextual understanding. AI systems are trained on specific datasets and can only make sense of information within the context of those datasets. They lack the ability to interpret newsituations or apply knowledge acquired in one context to another. In contrast, the human brain has the remarkable ability to draw connections between seemingly unrelated concepts and apply learned knowledge in novel situations.Another limitation of AI is its lack of empathy and emotional intelligence. While AI can simulate emotional responses, it lacks the ability to truly feel or understand emotions. Human beings are driven by emotions and motivated by a sense of purpose and meaning, which AI cannot replicate. This emotional intelligence is crucial in areas such as social interactions, emotional support, andcreative thinking.Moreover, AI is dependent on human beings for its design, programming, and supervision. While AI systems can make decisions independently, they require human input and oversight to ensure their ethical and responsible use. This dependence on humans for guidance and direction suggests that AI is not a replacement for the human brain but rather a tool that can augment human capabilities.In conclusion, while AI has made remarkable progress in simulating certain cognitive functions, it is unlikely to replace the human brain anytime soon. The human brain's unique combination of rational thinking, emotional intelligence, creativity, and adaptability sets it apart from any technological advancement. AI, instead of replacing the human brain, is more likely to serve as a complement, enhancing our abilities and expanding the boundaries of what is possible.As we continue to explore the potential of AI, it is important to remember that technology is a tool and should be used to augment human capabilities rather than replace them. The future of intelligence lies not in the replacement of the human brain but in the harmonious integration of AI and human thought, leading to new levels of understanding and innovation.。

chatgpt英语作文

chatgpt英语作文

chatgpt英语作文Chatbot GPT: A Revolution in Conversational AI。

Artificial Intelligence (AI) has been around for decades, but it's only in recent years that it has become a household term. From virtual assistants like Siri and Alexa to self-driving cars, AI has revolutionized the way we live and work. One of the most exciting applications of AI is chatbots, which are computer programs designed to simulate human conversation. In this article, we'll explore one of the most advanced chatbots in the world, GPT (Generative Pre-trained Transformer).What is GPT?GPT is an AI-powered chatbot developed by OpenAI, a research organization that aims to create safe and beneficial AI. GPT stands for Generative Pre-trained Transformer, which refers to the technology behind the chatbot. GPT uses a transformer-based neural networkarchitecture that allows it to generate human-like responses to text prompts. The chatbot is pre-trained on a massive corpus of text data, which enables it to understand the nuances of human language and generate coherent and contextually relevant responses.How does GPT work?GPT works by using natural language processing (NLP) to understand the meaning behind text prompts. When a user inputs a message, GPT analyzes the text to identify the context and intent behind the message. It then generates a response that is tailored to the user's input. GPT uses a probabilistic model to generate responses, which means that it generates multiple possible responses and selects the most likely one based on the context.What are the applications of GPT?GPT has a wide range of applications in various industries. One of the most popular applications of GPT is in customer service. Many companies use chatbots to handlecustomer queries and complaints. GPT-powered chatbots can provide customers with instant responses that are tailored to their specific needs. This can help companies save time and money by reducing the need for human customer service representatives.GPT can also be used in education to provide personalized learning experiences for students. The chatbot can analyze a student's learning style and provide them with customized learning materials. This can help students learn more efficiently and effectively.Another application of GPT is in healthcare. The chatbot can be used to provide patients with medical advice and information. GPT can analyze a patient's symptoms and provide them with a diagnosis and treatment options. This can help patients get the care they need more quickly and efficiently.What are the limitations of GPT?While GPT is an impressive technology, it has itslimitations. One of the biggest limitations of GPT is its inability to understand visual information. The chatbot relies solely on text prompts, which means that it cannot analyze images or videos. This can limit its applicationsin industries like e-commerce, where visual information is crucial.Another limitation of GPT is its susceptibility to bias. The chatbot is pre-trained on a massive corpus of text data, which means that it may pick up biases and stereotypes from the data. This can result in the chatbot generating responses that are biased or discriminatory.Conclusion。

激光陀螺捷联惯性导航系统的精密温控设计与验证

激光陀螺捷联惯性导航系统的精密温控设计与验证

2020年12月第6期现代导航·391·激光陀螺捷联惯性导航系统的精密温控设计与验证李金龙1,李邦立1,熊振宇2,魏国2,王林2,仲亚松3,陈位波4(1中国人民解放军91184部队,青岛266000;2国防科技大学,长沙410073;3国防大学,上海200433;4中国人民解放军78007部队,成都610000)摘要:在激光陀螺捷联惯性导航系统的工作过程中,外界环境温度的变化会对系统内加速度计的测量精度产生干扰,进而影响导航精度。

因此为了提高捷联惯性导航系统的导航精度,需要对其工作环境进行精密的温度控制。

根据对捷联惯性导航系统中加速度计的热学分析,可以得知当温控精度达到0.01℃时,加速度计的输出精度可以达到1×10-5m/s2。

本文对加速度计的误差特性进行了理论分析,同时搭建了一套多级精密温度控制系统,通过理论分析与基于实际温控系统的实验,验证了温度控制的理论并且研究了其在不同工作环境下的工作性能。

关键词:捷联惯性导航系统;误差模型;系统设计;多级温控中图分类号:TN249文献标识码:A文章编号:1674-7976-(2020)-06-391-05Design and Validation of Precision Temperature Control System forRing Laser Inertial Navigation SystemLI Jinlong,LI Bangli,XIONG Zhenyu,WEI Guo,WANG Lin,ZHONG Yasong,CHEN WeiboAbstract:In the Ring Laser Gyro Inertial Navigation System(RLG INS),the variation of working temperature can directly influence its measurement accuracy.In order to enhance the performance of the RLG INS,the operating temperature should be precisely controlled.According to thermal analysis on the accelerometer in the RLG INS,temperature control precision should be better than0.01℃to achieve1×10-5m/s2output accuracy of the accelerometer.This paper adopt Back Propagation Neural Network algorithm to simulate the accelerometer and set up a precise temperature control system.Based on the simulated model and the control system,experiments are designed to validate the feasibility of temperature control theory.Meanwhile the performance of temperature control system is also verified under different environment conditions.Key words:Strap-Down Inertial Navigation System;Error Model;System Design;Multi-Level Temperature Control0引言激光陀螺惯性导航系统是利用惯性敏感元件(激光陀螺和加速度计)测量载体相对惯性空间的线运动和角运动,并在已知的初始条件下,用计算收稿日期:2020-10-09。

基于深度学习的垃圾分类系统设计与实现

基于深度学习的垃圾分类系统设计与实现
2.1.1 卷积层.................................................................................................. 9 2.1.2 池化层................................................................................................ 10 2.1.3 激活函数............................................................................................ 11 2.1.4 全连接/Softmax................................................................................. 13 2.1.5 误差反向传播.................................................................................... 14 2.2 图像分类模型............................................................................................... 15 2.2.1 AlexNet 模型.......................................................................................15 2.2.2 VGGNet 和 GoogleNet 模型.............................................................. 16 2.2.3 ResNet................................................................................................. 18 2.3 基于区域提名的目标检测模型................................................................... 19 2.3.1 R-CNN 和 SPP-net 模型.....................................................................19 2.3.2 Fast R-CNN 模型................................................................................21 2.3.3 Faster R-CNN 模型.............................................................................22 2.4 端到端的目标检测模型............................................................................... 24 2.4.1 YOLO 模型......................................................................................... 24 2.4.2 SSD 模型.............................................................................................25 2.5 目标检测评价指标....................................................................................... 27 2.6 深度学习框架 PyTorch................................................................................ 28 2.7 本章小结....................................................................................................... 28

稻谷干燥模型数学模拟与神经网络仿真

稻谷干燥模型数学模拟与神经网络仿真

稻谷干燥模型数学模拟与神经网络仿真摘要我国是一个人口大国.同时也是一个农业大国,我国的粮食总产景近年大约平均有5亿吨,每年收获的粮食有20%属高水分.约有8500y如电需要干燥。

如何使一年的劳动成果颗粒归仓,保证粮食丰产又丰收,是一个重要的研究课题。

为了对那些旧的、老式的、自动化程度不高的、烘干后粮食品质不好的谷物干燥机进行改进,则需要将现代化科技手段应用到粮食干燥过程的研究中来。

随着计算机技术的飞速发展,把计算机技术应用到粮食干燥过程的研究_L是司行的,是大势所趋。

国外对稻谷干燥问题进行了大量的研究工作,在单粒和薄层干燥研究的基础上,又开始了有实用价值的稻貉深床干燥的研究,提…了~‘些稻谷干燥的数学模型。

国内对稻谷干燥的研究从80年代开始。

一些专家、学者根据我国国情,在稻谷干燥理论和干燥技术上做了大量探讨,为设计适合我国特点的稻谷干燥系统提供了理论上的依据。

本课题以稻谷作为实验对象,根据前人所建立的稻谷单粒、薄层和深床三种干燥数学模型编写了稻谷干燥过程模拟计算程序,进行计算机模拟。

利用该程序可预测稻谷在改变干燥介质(空气)的入口温度和湿度、被干燥物料的初始湿含量和温度及床层厚度等因素的情况下,粮层中各点的湿含量和温度、干燥介质的出口湿度和温度随时间的变化,以此来研究上述几种因素对湿物料干燥速率的影响,并由此得到一个干燥物料(稻谷)的较合理的工艺过程,并可对谷物机械热风干燥的性能进行综合分析研究。

同时利用以上过程得出的计算机数学模拟结果,应用神经网络理论建立数学模型,对干燥过程进行仿真,为进一步实现干燥过程的计算机模糊控制提供依据。

系统软件应用MATLAB语言进行编程,程序简单易懂,界面清晰,可操作性强,模拟计算结果可以以曲线的形式输出,直观明了。

本课题以计算机和强大的应用软件MATLAB为手段,应用数值分析、热力学和神经网络等先进理论,对稻谷的干燥过程进行了数学模拟和神经网络仿真,为寻求稻谷干燥过程的最优化及全自动控制,提高我国的谷物干燥技术和粮食产品质量,增强我国粮食产品在国际市场上的竞争力具有十分重大意义。

【英语词汇】 imitate、mimic、mock、 simulate、emulate 这组词都有

【英语词汇】 imitate、mimic、mock、 simulate、emulate 这组词都有

【英语词汇】imitate、mimic、mock、simulate、emulate 这组词都有imitate 指出于仰慕或缺乏独创性而模仿他人或某物(模仿核心部分,但在细节上却不一定一样)。

其名词是imitation。

1)Tom admired the film star Jerry very much and he liked to imitate every action of his. 汤姆非常钦佩的电影杰里,喜欢模仿他的每一个动作。

2)Teachers should provide a model for children to imitate. 教师应该是孩子们仿效的典范。

3)Some birds, as the parrot, can imitate human voice. 有些鸟儿,如鹦鹉,能模仿人的声音。

4)You use computer programmes to imitate the real world and call it “virtual reality”. 你可用计算机程序模拟现实世界,称它为“虚拟现实”。

5)Painters lacking originality often spent his lives in the imitation of the great masters. 缺乏创意的画家通常一身都在临摹绘画大师的作品。

6)An expert was needed to authenticate the original Van Gogh painting from his imitation. 这幅画是凡高的真迹还是赝品,需由专家来鉴定。

mimic 指以开玩笑或取笑的目的模仿他人,尤指滑稽模仿他人的言行举止。

也可指模仿物的式样、特征、性质等。

1)The naughty boy mimicked his teacher’s voice and gestures very cleverly. 这个淘气的男孩把他老师的声音和姿态模仿得惟妙惟肖。

人工智能应用英语作文常用的高级词汇和句式附加句式翻译练习

人工智能应用英语作文常用的高级词汇和句式附加句式翻译练习

人工智能应用英语作文常用的高级词汇和句式附加句式翻译练习人工智能(AI)已经成为当今的热门话题。

下面是一些关于人工智能应用英语作文常用的高级词汇和句式,以及附加句式翻译练,帮助你更好地写作。

高级词汇- Artificial intelligence (AI):人工智能- Machine learning:机器研究- Neural network:神经网络- Big data:大数据- Deep learning:深度研究- Natural language processing (NLP):自然语言处理- Robotics:机器人技术- Expert system:专家系统- Augmented reality (AR):增强现实- Virtual reality (VR):虚拟现实- Data mining:数据挖掘- Internet of Things (IoT):物联网高级句式1. 引入句式- In recent years, AI has emerged as a revolutionary technology. 近年来,人工智能已经成为一项革命性的技术。

2. 表达观点句式- From my perspective, AI has the potential to change the world. 从我的角度来看,人工智能有改变世界的潜力。

- It is my belief that AI is an indispensable part of our future. 我相信,人工智能是我们未来不可或缺的一部分。

- In my opinion, AI will bring significant benefits to society. 在我看来,人工智能将为社会带来巨大的收益。

- Personally, I think AI is one of the most important technological advancements of our time. 就我个人而言,我认为人工智能是我们这个时代最重要的技术进步之一。

学习笔记之1001InventionsThatChangedtheWorld

学习笔记之1001InventionsThatChangedtheWorld

学习笔记之1001InventionsThatChangedtheWorld1001 Inventions That Changed the World: Jack Challoner: 9780764161360: : Bookshttps:///1001-Inventions-That-Changed-World/dp/0764161369https:///subject/4084287/We take thousands of inventions for granted, using them daily and enjoying their benefits. But how much do we really know about their origins and development? This absorbing new book tells the stories behind the inventions that have changed the world, with details about--Convenience items, such as safety pins, toothbrushes, and bifocalsWeapons of war, including explosives, gunpowder, and shrapnel shellsIndustrial advances, such as the steam engine and the power loom for weavingTransportation advances, including the airplane, the diesel engine, the automobile, and the air-inflated rubber tireElectronic marvels, including color television, the microprocessor, the personal computer, the compact disc, and the cell phone Medical advances, from antiseptic surgery to the electron microscope. . . and much more.Inventors and pioneers of science and technology, including Eli Whitney, James Watt, Benjamin Franklin, Henry Bessemer, Thomas Edison, J.B. Dunlop, the Wright Brothers, Werner von Braun, Jonas Salk, J. Robert Oppenheimer, and many others are alsodiscussed. Fascinating photos and illustrations complement authoritative summaries of each invention, and remarkable quotations from many of the inventors add to this chronicle of human ingenuity that began some 6,000 years ago with the invention of the wheel.Approximately 700 photos and illustrations in color and black and white.顺着时间线⼀路读下来,就像在看⼈类科学技术的发展史,看这些改变⼈类的伟⼤发明当初是如何实现的。

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* Corresponding author. Phone: +34 953 648526. Fax: +34 953 648508. E-mail: mpdorado@ujaen.es. † Department of Electronic, Telecommunication, and Automatic Engineering. ‡ Department of Mechanics and Mining Engineering. (1) Dorado, M. P.; Ballesteros, E.; Arnal, J. M.; Go ´ mez, J.; Lo ´ pez, F. J. Energy Fuels 2003, 17, 1560-1565. (2) Geyer, S. M.; Jacobus, M. J.; Lestz, S. S. Trans. ASAE 1984, 27, 375-384. (3) Peterson, C. L. Trans. ASAE 1986, 29, 1413-1422. (4) Dorado, M. P.; Ballesteros, E.; Arnal, J. M.; Go ´ mez, J.; Lo ´ pez, F. J. Fuel 2003, 82, 1311-1315. (5) Otera, J. Chem. ReV. 1993, 93, 1449-1470.
and oils provides a costly fuel compared to diesel fuel. In fact, it is reported that approximately 70-95% of the final cost of biodiesel arises from the cost of the raw materials.6,7 So, to decrease the final cost of biodiesel, it is important to select inexpensive or low-cost raw materials, that is, used frying vegetable oil.8-11 On the other hand, time-consuming and costly laboratory tests are required to conduct the optimization of the parameters involved in the transesterification of the oils or fats, at each working condition. For this reason, it is of interest to implement a simulation process to accurately predict the biodiesel yield evolution while varying the initial condition values, thus, substituting part of the laboratory tests. Along these lines, an artificial neural network (ANN) model is an abstract simulation of a real nervous system that contains a collection of neuron units communicating with each other via axon connections. This technique is able to handle incomplete data, to deal with nonlinear problems, and once trained can perform predictions and generalizations at high speeds. Since the first fundamental modeling of neural nets was proposed in
Energy & Fuels 2006, 20, 399-402
399
A Neural Network Approach to Simulate Biodiesel Production from Waste Olive Oil
Antonio J. Yuste† and M. Pilar Dorado,*,‡
Introduction Most developed countries are becoming increasingly aware of the importance of environmental preservation. In this sense, to achieve a high level of environmental protection, environmental policies are promoting the use of renewable energies, punishing the emission of pollutants, and financing the research and use of renewable energies. Among many other alternative fuels for diesel engines that are derived from vegetable oils in its pure form, blended with diesel fuel or alcohol, winterized, and so forth, the one called biodiesel is the most widely accepted alternative to diesel fuel because of the similarity in its properties when compared to those of diesel fuel.1-3 Also, this alternative fuel for diesel engines emits a lower amount of the most regulated pollutants compared to diesel fuel.4 Biodiesel is produced by the transesterification of triglycerides (which can be found in animal fats and vegetable oils) with an alcohol in the presence of a homogeneous or heterogeneous catalyst. This reaction results in the production of monoalkyl esters, also called biodiesel, and glycerol as a byproduct.5 One of the main problems related to the wide acceptance of biodiesel is its economic viability. In this sense, to extend the use of biodiesel, it is important to decrease the costs related to biodiesel production. Otherwise, the transesterificationountries have environmental policies that promote the development and application of renewable energies, and among these is biodiesel. However, the optimization of the chemical reaction that is required to produce biodiesel, called transesterification, is a costly and time-consuming process that needs expensive reactants and laboratory equipment. In this context, an artificial neural network (ANN) model has been developed to simulate biodiesel production through the transesterification of used frying olive oil. Afterward, the model was validated with sets of experimental data obtained from the laboratory and that were not used during the training procedure. In this sense, simulated results were similar to those obtained with the help of the classical empirical tests required to perform the transesterification process in a laboratory, thus, indicating the simulated biodiesel yield function has properly reflected the real process. We can conclude that ANNs can be used to predict the biodiesel yield from used olive oil.
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