Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering / Edition 1

by Hongxing Li, C.L. Philip Chen, Han-Pang Huang

Overview

Although 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:

Fundamental concepts and theories for fuzzy systems and neural networks.

Foundation for fuzzy neural networks and important related topics

Case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems

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.

Product Details

ISBN-13:	9780849323607
Publisher:	Taylor & Francis
Publication date:	09/21/2000
Edition description:	New Edition
Pages:	392
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Hongxing Li, C.L. Philip Chen, Han-Pang Huang

Table of Contents

Foundation of Fuzzy Systems. Determination of Membership Functions. Mathematical Essence and Structures of Feedforward Artificial Neural Networks. Functional-Link Neural Networks and Visualization Means of Some Mathematical Methods. Flat Neural Networks and Rapid Learning Algorithms. Basic Structure of Fuzzy Neural Networks. Mathematical Essence and Structures of Feedback Neural Networks and Weight Matrix Design. Generalized Additive Multifactorial Function and Its Applications to Fuzzy Inference and Neural Networks. The Interpolation Mechanism of Fuzzy Control. The Relationship between Fuzzy Controllers and PID Controllers. Adaptive Fuzzy Controllers Based on Variable Universes. The Basics of Factor Spaces. Neuron Models Based on Factor Spaces Theory and Factor Space Canes. Foundation of Neuro-Fuzzy Systems and an Engineering Application. Data Preprocessing. Control of a Flexible Robot Arm Using a Simplified Fuzzy Controller. Application of Neuro-Fuzzy Systems: Development of a Fuzzy Learning Decision Tree and Application to Tactile Recognition. Fuzzy Assessment Systems of Rehabilitative Process for CVA Patients. A DSP-Based Neural Controller for a Multi-Degree Prosthetic Hand. Index.

Editorial Reviews

Authors, Li (Beijing Normal U.), G.L. Philip Chen (Wright State U., Dayton), and Han-Pang Huang (National Taiwan U.), present the mathematical foundation for fuzzy neural networks and a format for combining neural networks with fuzzy logic systems. The intended audience includes engineers, scientists and researchers, as well as graduate students who have had linear algebra and engineering mathematics. The final five (of 19) chapters apply the mathematical foundation to extensive case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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