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

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More About This Textbook

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.

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Editorial Reviews

Booknews
Unlike traditional, hard computing, this field aims to accommodate the pervasive imprecision of the real world, using the human mind as its role model. Intended for use as a graduate level text, or a self- study guide for students and researchers, this book presents an introduction to the field, and then covers fuzzy set theory, regression and optimization, neutral networks, neuro-fuzzy modeling and controls, and advanced applications. It also includes hints to selected exercises, and lists of Internet resources, MATLAB programs, and acronyms. Contains an offer for free companion software. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780132610667
  • Publisher: Prentice Hall
  • Publication date: 9/26/1996
  • Series: MATLAB Curriculum Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 614
  • Product dimensions: 7.00 (w) x 9.10 (h) x 1.30 (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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