Neuro-fuzzy and soft computing : a computational approach to learning and machine intelligence

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.

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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Table of Contents