## Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in EngineeringAlthough fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations. Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications: Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field. |

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### Contents

1 | |

Determination of Membership Functions | 23 |

Mathematical Essence and Structures of Feedforward Artificial Neural Networks | 47 |

Functionallink Neural Networks and Visualization Means of Some Mathematical Methods | 72 |

Flat Neural Networks and Rapid Learning Algorithms | 90 |

Basic Structure of Fuzzy Neural Networks | 113 |

Mathematical Essence and Structures of Feedback Neural Networks and Weight Matrix Design | 126 |

Generalized Additive Weighted Multifactorial Function and its Applications to Fuzzy Inference and Neural Networks | 140 |

Adaptive Fuzzy Controllers Based on Variable Universes | 181 |

The Basics of Factor Spaces | 197 |

Neuron Models Based on Factor Spaces Theory and Factor Space Canes | 219 |

Foundation of NeuroFuzzy Systems and an Engineering Application | 241 |

Data Preprocessing | 255 |

Control of a Flexible Robot Arm using a Simplified Fuzzy Controller | 267 |

Application of NeuroFuzzy Systems Development of a Fuzzy Learning Decision Tree and Application to Tactile Recognition | 295 |

Fuzzy Assessment Systems of Rehabilitative Process for CVA Patients | 322 |

The Interpolation Mechanism of Fuzzy Control | 152 |

The Relationship between Fuzzy Controllers and PID Controllers | 165 |

A DSPbased Neural Controller for a Multidegree Prosthetic Hand | 351 |

### Other editions - View all

Fuzzy Neural Intelligent Systems: Mathematical Foundation and the ... Hongxing Li,C.L. Philip Chen,Han-Pang Huang No preview available - 2000 |

### Common terms and phrases

A/P direction activation functions Adaptive Fuzzy Controllers ANFIS applications artificial neural networks chapter coefficients comparison computation control ability CVA patients data preprocessing decision tree defined defuzzification degree of membership denoted discussed EMG signal enhancement nodes Equation error example Expression factor space cane Feature Extraction feedforward neural network flexible arm fuzzy concept fuzzy inference fuzzy logic fuzzy model fuzzy neural network fuzzy sets fuzzy statistical Fuzzy Systems given hidden layer identity function IEEE Transactions input variables interpolation function kinetic state assessment learning algorithm linear programming M/L direction mapping means membership function method model of neurons monotonic multifactorial function neuro-fuzzy nonlinear normal object on-line output parameters PID controller piecewise points problem Proof pseudoinverse rule base satisfies shown in Figure sigmoid function simplified fuzzy control Step t-norm Table tactile sensing Theorem threshold values time-series training data update vector weight matrix