## Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with ApplicationsOkyay Kaynak, Lotfi A. Zadeh, Burhan Türksen, Imre J. Rudas Soft computing is a consortium of computing methodologies that provide a foundation for the conception, design, and deployment of intelligent systems and aims to formalize the human ability to make rational decisions in an environment of uncertainty and imprecision. This book is based on a NATO Advanced Study Institute held in 1996 on soft computing and its applications. The distinguished contributors consider the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications. Two areas emphasized in the book are how to achieve a synergistic combination of the main constituents of soft computing and how the combination can be used to achieve a high Machine Intelligence Quotient. |

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### Contents

Computational Intelligence | 1 |

Computational Intelligence Defined By Everyone | 10 |

Computational Intelligence Extended Truth Tables and Fuzzy Normal Forms | 38 |

Uncertainty Theories by Modal Logic | 60 |

Foundations of Fuzzy Theory | 80 |

Measures of Specificity | 94 |

Whats in a Fuzzy Membership Value? | 114 |

New Types of Generalized Operations | 128 |

The Morphogenetic Neuron | 304 |

Boolean Soft Computing by Nonlinear Neural Networks With Hyperincursive Stack Memory | 333 |

Data Analysis | 352 |

Fuzzy Data Analysis | 381 |

Probabilistic and Possibilistic Networks and How To Learn Them from Data | 403 |

Applications | 427 |

Fuzzy Sets and the Management of Uncertainty in Computer Vision | 434 |

Intelligent Robotic Systems Based on Soft Computing Adaptation Learning and Evolution | 450 |

Fuzzy Systems | 157 |

Fuzzy Inference Systems A Critical Review | 177 |

Fuzzy Decision Support Systems | 198 |

NeuroFuzzy Systems | 230 |

Fuzzified PetriNets and Their Application to Organising Supervisory Controller | 260 |

Neural Networks | 283 |

Hardware and Software Architectures for Soft Computing | 482 |

Fuzzy Logic Control for Design and Control of Manufacturing Systems | 496 |

Applications Of Intelligent Multiobjective Fuzzy Decision Making | 514 |

A Product Life Cycle Information Management System Infrastructure with CADCAECAM Task Automation and Intelligent Support Capabilities | 521 |

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### Common terms and phrases

antigen applications approach approximate reasoning architecture basis functions behavior classifier Computational Intelligence computer vision constraints crisp decision decomposition defined definition defuzzification denote Dubois entropy equation example expressions fuzzy clustering fuzzy controller fuzzy inference fuzzy logic fuzzy logic control fuzzy rules fuzzy set theory fuzzy subsets fuzzy system Genetic Algorithm given GLVQ-F hierarchical IEEE IEEE Trans input variables intelligent control intelligent systems interpretation intersection knowledge labeled learning algorithm linear linguistic manufacturing systems mapping mathematical membership functions membership grade membership values method modal logic morphogenetic neuron neural network neuro-fuzzy neuro-fuzzy system neuron objective obtained operators optimal output parameters pattern recognition possibilistic possibility distributions probabilistic problem propositions prototypes relation representation represented robotic system Sets and Systems soft computing solution Sp(A structure supervised learning t-norm Theorem training data uncertainty vector weights Yager Zadeh

### References to this book

Graphical Models: Methods for Data Analysis and Mining Christian Borgelt,Rudolf Kruse No preview available - 2002 |