Soft Computing in Systems and Control TechnologySoft 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. |
Contents
The backpropagation algorithm | 19 |
Chapter 2 | 31 |
Implementation issues | 32 |
Simulation results | 43 |
Concluding remarks | 50 |
Feedforward networks | 57 |
Recurrent networks | 65 |
Weightless networks | 71 |
Applications of genetic algorithms | 251 |
Problem Approach | 281 |
Conclusions | 289 |
A pattern mode training algorithm | 295 |
Robustness of the Approach | 301 |
Network configuration IDANET | 307 |
Fish distributions | 317 |
Conclusions | 324 |
Introduction | 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 |
55 | 155 |
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 |
Modifications to the simple GA | 239 |
An overview | 330 |
Example applications of EAS in CAD | 332 |
Performance evaluation | 338 |
Conclusions | 346 |
Chapter 12 | 355 |
An illustrative example | 367 |
Chapter 13 | 379 |
An application of neural network technology to the COMMONS game | 387 |
Concluding remarks | 393 |
Fundamentals of coherent neural networks | 399 |
Stability of coherent neural networks for timesequential signal processing | 414 |
Conclusions | 421 |
Neural systems review | 427 |
A control system application study | 435 |
Chapter 16 | 445 |
Neural techniques for contaminants in ambient air | 453 |
Neural techniques for aqueous contaminants | 459 |
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Common terms and phrases
adaptive applications approach approximation architecture arm-configurations artificial neural networks chromosomes control input controlled system corresponding criterion crossover defined Digital dwmn dynamic encoding evaluation example Figure filter fitness values fuzzy ART fuzzy implication membership fuzzy logic fuzzy model fuzzy rules fuzzy sets genetic algorithm heuristic hidden layer hidden neurons Hopfield network hyperbox identification IEEE IEEE Trans implication membership function initial input space input variables iterations learning rate linear matrix mean square error membership function method minimize mobile robot mutation n-tuple NBAR neural network neuro-fuzzy neurofuzzy neurons node obtained operator optimal parameters path pattern performance index population position predictive control problem Proc proposed Radial Basis Function random recurrent representation represents response Riccati equation robust sample schema Section selection self-tuning sensors shown in Fig sigmoid function signal simulation string structure techniques testing tree Tzafestas units updating vector