# A First Course in Fuzzy and Neural Control (Google eブックス)

CRC Press, 2002/11/12 - 312 ページ
Although the use of fuzzy control methods has grown nearly to the level of classical control, the true understanding of fuzzy control lags seriously behind. Moreover, most engineers are well versed in either traditional control or in fuzzy control-rarely both. Each has applications for which it is better suited, but without a good understanding of both, engineers cannot make a sound determination of which technique to use for a given situation.

A First Course in Fuzzy and Neural Control is designed to build the foundation needed to make those decisions. It begins with an introduction to standard control theory, then makes a smooth transition to complex problems that require innovative fuzzy, neural, and fuzzy-neural techniques. For each method, the authors clearly answer the questions: What is this new control method? Why is it needed? How is it implemented? Real-world examples, exercises, and ideas for student projects reinforce the concepts presented.

Developed from lecture notes for a highly successful course titled The Fundamentals of Soft Computing, the text is written in the same reader-friendly style as the authors' popular A First Course in Fuzzy Logic text. A First Course in Fuzzy and Neural Control requires only a basic background in mathematics and engineering and does not overwhelm students with unnecessary material but serves to motivate them toward more advanced studies.

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### 目次

 A PRELUDE TO CONTROL THEORY 1 12 Examples of control problems 3 122 Closedloop control systems 5 13 Stable and unstable systems 9 14 A look at controller design 10 15 Exercises and projects 14 MATHEMATICAL MODELS IN CONTROL 15 inverted pendulum on a cart 20
 311 Universal approximation 126 312 Exercises and projects 128 FUZZY CONTROL 133 42 Main approaches to fuzzy control 137 421 Mamdani and Larsen methods 139 422 Modelbased fuzzy control 140 43 Stability of fuzzy control systems 144 44 Fuzzy controller design 146

 22 State variables and linear systems 29 23 Controllability and observability 32 24 Stability 34 241 Damping and system response 36 242 Stability of linear systems 37 243 Stability of nonlinear systems 39 244 Robust stability 41 25 Controller design 42 26 Statevariable feedback control 48 262 Higherorder systems 50 27 Proportionalintegralderivative control 53 temperature control 61 controlling dynamics of a servomotor 71 28 Nonlinear control systems 77 29 Linearization 78 210 Exercises and projects 80 FUZZY LOGIC FOR CONTROL 85 32 Fuzzy sets in control 86 33 Combining fuzzy sets 90 332 Triangular norms conorms and negations 92 333 Averaging operators 101 34 Sensitivity of functions 104 342 Average sensitivity 106 35 Combining fuzzy rules 108 351 Products of fuzzy sets 110 353 Larsen model 111 354 TakagiSugenoKaiig TSK model 112 355 Tsukamoto model 113 36 Truth tables for fuzzy logic 114 37 Fuzzy partitions 116 38 Fuzzy relations 117 381 Equivalence relations 119 382 Order relations 120 392 Heightcenter of area method 121 393 Max criterion method 122 395 Middle of maxima method 123 3101 Extension principle 124 3102 Images of alphalevel sets 125
 controlling dynamics of a servomotor 151 45 Exercises and projects 157 NEURAL NETWORKS FOR CONTROL 165 52 Implementing neural networks 168 53 Learning capability 172 54 The delta rule 175 55 The backpropagation algorithm 179 training a neural network 183 training a neural network 185 58 Practical issues in training 192 59 Exercises and projects 193 NEURAL CONTROL 201 62 Inverse dynamics 202 63 Neural networks in direct neural control 204 641 A neural network for temperature control 205 642 Simulating PI control with a neural network 209 65 Neural networks in indirect neural control 216 651 System identification 217 system identification 219 653 Instantaneous linearization 223 66 Exercises and projects 225 FUZZYNEURAL AND NEURALFUZZY CONTROL 229 71 Fuzzy concepts in neural networks 230 72 Basic principles of fuzzyneural systems 232 73 Basic principles of neuralfuzzy systems 236 731 Adaptive network fuzzy inference systems 237 732 ANFIS learning algorithm 238 74 Generating fuzzy rules 245 75 Exercises and projects 246 APPLICATIONS 249 82 Cooling scheme for laser materials 250 83 Color quality processing 256 84 Identification of trash in cotton 262 85 Integrated pest management systems 279 86 Comments 290 Bibliography 291 Index 297 著作権

### 人気のある引用

291 ページ - Machine Learning— Neural Networks, Genetic Algorithms, and Fuzzy Systems, John Wiley & Sons, New York.
292 ページ - IR Goodman, HT Nguyen, and EA Walker. Conditional Inference and Logic for Intelligent Systems: a Theory of Measure-Free Conditioning. North-Holland, Amsterdam, 1991.

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