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Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence / Edition 1
     

Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence / Edition 1

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by Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, Jang
 

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ISBN-10: 0132610663

ISBN-13: 9780132610667

Pub. Date: 09/26/1996

Publisher: Pearson

Neuro-Fuzzy Modeling and Soft Computing places particular emphasis on the theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. Neuro-Fuzzy Modeling and Soft Computing is oriented toward methodologies that are likely to be of practical use. It includes

Overview

Neuro-Fuzzy Modeling and Soft Computing places particular emphasis on the theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. Neuro-Fuzzy Modeling and Soft Computing is oriented toward methodologies that are likely to be of practical use. It includes exercises, some of which involve MATLAB programming tasks to provide readers with hands-on programming experiences for practical problem-solving. Each chapter also includes a reference list to the research literature so that readers may pursue topics in greater depth. This book is suitable as a self-study guide by researchers who want to learn basic and advanced neuro-fuzzy and soft computing within the framework of computational intelligence.

Product Details

ISBN-13:
9780132610667
Publisher:
Pearson
Publication date:
09/26/1996
Series:
MATLAB Curriculum Series
Edition description:
New Edition
Pages:
614
Product dimensions:
7.00(w) x 9.10(h) x 1.50(d)

Table of Contents

1. Introduction to Neuro-Fuzzy 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. Least-Squares Methods for System Identification.

6. Derivative-Based Optimization.

7. Derivative-Free Optimization.

III. NEURAL NETWORKS.

8. Adaptive Networks.

9. Supervised Learning Neural Networks.

10. Learning from Reinforcement.

11. Unsupervised Learning and Other Neural Networks.

IV. NEURO-FUZZY MODELING.

12. ANFIS: Adaptive-Networks-based Fuzzy Inference Systems.

13. Coactive Neuro-Fuzzy Modeling: Towards Generalized ANFIS.

V. ADVANCED NEURO-FUZZY MODELING.

14. Classification and Regression Trees.

15. Data Clustering Algorithms.

16. Rulebase Structure Identification.

VI. NEURO-FUZZY CONTROL.

17. Neuro-Fuzzy Control I.

18. Neuro-Fuzzy Control II.

VII. ADVANCED APPLICATIONS.

19. ANFIS Applications.

20. Fuzzy-Filtered Neural Networks.

21. Fuzzy Theory and Genetic Algorithms in Game Playing.

22. Soft Computing for Color Recipe Prediction.

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