A First Course in Fuzzy and Neural Control (Google eBook)

Front Cover
CRC Press, Nov 12, 2002 - Mathematics - 312 pages
1 Review
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.
  

What people are saying - Write a review

Review: A First Course in Fuzzy and Neural Control

User Review  - Moufid - Goodreads

good introduction to Fuzzy and neuronal Control Read full review

Contents

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
Copyright

Common terms and phrases

Popular passages

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

References to this book

All Book Search results »

Bibliographic information