
NeuroFuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, 1/e
JyhShing Roger Jang, TsingHua University
ChuenTsai Sun, National Chiao Tung University
Eiji Mizutani, Kansai Paint Company
Published September, 1996 by Prentice Hall Engineering/Science/Mathematics
Copyright 1997, 614 pp.
Cloth
ISBN 0132610663

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This text provides the first comprehensive treatment of the methodologies
underlying neurofuzzy and soft computing, an evolving branch within
the scope of computational intelligence. The book places equal emphasis
on theoretical aspects of covered methodologies, empirical observations
and verifications of various applications in practice.
The book is oriented toward methodologies that are likely
to be of practical use; many stepbystep examples are included to
complement explanations in the text.
Specially designed figures provide a visual reinforcement
for as many ideas and concepts as possible. These figures were generated
using MATLAB and these MATLAB files are available via FTP or WWW.
Includes exercises, some of which involve MATLAB programming
tasks which can be expanded into suitable term projects. This will
provide the student with handson programming experiences for practical
problemsolving.
Each chapter includes a reference list to the research literature.
This will enable students to pursue individual topics in greater depth.
1. Introduction to NeuroFuzzy and Soft Computing.
I. FUZZY SET THEORY.
2. Fuzzy Sets.
3. Fuzzy Rules and Fuzzy Reasoning.
4. Fuzzy Inference Systems.
II. REGRESSION AND OPTIMIZATION.
5. LeastSquares Methods for System Identification.
6. DerivativeBased Optimization.
7. DerivativeFree Optimization.
III. NEURAL NETWORKS.
8. Adaptive Networks.
9. Supervised Learning Neural Networks.
10. Learning from Reinforcement.
11. Unsupervised Learning and Other Neural Networks.
IV. NEUROFUZZY MODELING.
12. ANFIS: AdaptiveNetworksbased Fuzzy Inference Systems.
13. Coactive NeuroFuzzy Modeling: Towards Generalized ANFIS.
V. ADVANCED NEUROFUZZY MODELING.
14. Classification and Regression Trees.
15. Data Clustering Algorithms.
16. Rulebase Structure Identification.
VI. NEUROFUZZY CONTROL.
17. NeuroFuzzy Control I.
18. NeuroFuzzy Control II.
VII. ADVANCED APPLICATIONS.
19. ANFIS Applications.
20. FuzzyFiltered Neural Networks.
21. Fuzzy Theory and Genetic Algorithms in Game Playing.
22. Soft Computing for Color Recipe Prediction.
