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. 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. |
From inside the book
Results 1-5 of 68
... neuron models and neural networks formed by factor spaces . Chapter 14 gives the foundation of neuro - fuzzy systems . Chapter 15 explores the nature of data and discusses the importance of data preprocessing . Chapters 16 to 18 give ...
... Neurons and Mathematical Neural Networks , 48 3.2.1 MP Model with Discrete Outputs , 48 3.2.2 MP Model with Continuous - valued Outputs , 48 3.3 The Interpolation Mechanism of Feedforward Neural Networks , 52 3.4 A Three - layer ...
... Neurons , 113 6.2 Fuzzy Neural Networks , 118 6.2.1 Neural Network Representation of Fuzzy Relation Equations , 118 6.2.2 A Fuzzy Neural Network Based on FN ( v , ^ ) , 119 6.3 A Fuzzy & Learning Algorithm , 121 6.4 The Convergence of ...
... Neurons without Respect to Time , 220 13.2.1 Threshold Models of Neurons , 220 13.2.2 Linear Model of Neurons , 221 13.2.3 General Threshold Model of Neurons , 221 13.2.4 The Models of Neurons Based on Weber - Fechner's Law , 223 197 ...
... Neurons Concerned with Time , 224 13.4 The Models of Neurons Based on Variable Weights , 225 13.4.1 The Excitatory and Inhibitory Mechanism of Neurons , 225 13.4.2 The Negative Weights Description of the Inhibitory Mechanism , 226 13.4 ...
Contents
1 | |
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 |