## 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. |

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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 |

477 | |

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### Common terms and phrases

activation function adaptive applications approach approximation architecture arm-configurations binary chromosomes connections controlled system convergence corresponding crossover defined Digital Filters dynamic encoding equation example feedforward Figure fitness values fuzzy ART fuzzy implication membership fuzzy logic fuzzy model fuzzy rules fuzzy sets genetic algorithm gradient heuristic hidden layer hidden neurons Hopfield network hyperbox identification IEEE IEEE Trans implication membership function impulse response initial input space iterations learning rate linear LSS model matrix mean square error membership function method minimize mobile robot mutation NBAR neural network neuro-fuzzy neurofuzzy node nonlinear obtained operator optimal output neurons parameters path perceptron population position predictive control problem Proc Radial Basis Function random representation represents Riccati equation robust Section selection sensors separable in denominator shown in Fig sigmoid function signal simulation string structure supervised learning techniques training set trajectory tree tuple Tzafestas uncertainty units updating vector