Soft Computing in Systems and Control Technology

Front Cover
World Scientific, 1999 - Computers - 479 pages
Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.
 

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Contents

Chapter
3
Supervised learning in multilayer perceptrons
13
The backpropagation algorithm
19
Chapter 2
31
Chapter 4
32
Implementation issues
42
Concluding remarks
50
Feedforward networks
57
Modifications to the simple GA
239
Applications of genetic algorithms
251
Conclusions
289
Results from a Variety of Genetic Algorithm Applications Showing
301
Network configuration IDANET
307
Fish distributions
317
Conclusions
324
An overview
330

Weightless networks
71
NeuroBased Adaptive Regulator
77
Multilayer neural network and learning
84
Conclusion
95
Continuoustime local model networks LMN
102
Conclusions
113
An OnLine Self Constructing Fuzzy Modeling Architecture Based on Neural
119
The basic TakagiSugenoKang model
125
Membership functions
138
Conclusion
153
Introduction
169
Model based control
175
Neurofuzzy model based control of a laboratory scale heat exchanger
184
Conclusion
191
Fuzzy and neurofuzzy robot path planning and navigation
198
Mobile robot motion planning and control
205
Some illustrative examples
211
Chapter 9
223
Example applications of EAs in CAD
332
Performance evaluation
338
Conclusions
346
Chapter 12
355
An illustrative example
367
Conclusions
375
Computer simulation
390
Stability of coherent neural networks for timesequential signal processing
414
Chapter 15
423
Evolutionary and genetic algorithms
431
Conclusions and outlook
438
Chapter 16
445
Neural techniques for contaminants in ambient air
453
Neural techniques for aqueous contaminants
459
Conclusion
464
Index
477
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