Computational Intelligence: An Introduction

Computational intelligence as a new development paradigm of intelligent systems has resulted from a synergy between neural networks, fuzzy sets, and genetic computations. This emerging area, even at its very earliest stage, has already attracted the attention of top researchers and practitioners. Computational Intelligence: An Introduction delivers a highly readable and fully systematic treatment of the fundamentals of CI, along with the clear presentation of sound and comprehensive analysis and design practices.
This text pulls together much of the scattered information written about this emerging field. Most publications dealing with CI are highly specialized and concentrate narrowly on the symbiosis between NN, FS, and GAs. Computational Intelligence: An Introduction bridges the gap between all three areas and CI. This is an important text for anyone engaged in any way with genetic algorithms, fuzzy sets, neural networks, and computational intelligence.

Table of Contents:
Chapter 1. Preliminaries
Computational Intelligence: Its Inception and Research Agenda
Organization and Readership
References
Chapter 2. Neural Networks and Neurocomputing
Introduction
Generic Models of Computational Neurons
Architectures of Neural Networks - A Basic Taxonomy
Learning in Neural Networks
Selected Classes of Learning Methods
Generalization Abilities of Neural Networks
Enhancements of Gradient-Based Learning in Neural Networks
Concluding Remarks
Problems
References
Chapter 3. Fuzzy Sets
Introduction
Basic Definition
Types of Membership Functions
Characteristics of a Fuzzy Set
Membership Function Determination
Fuzzy Relations
Set Theory Operations and Their Properties
Triangular Norms
Triangular Norms as the Models of Operations on Fuzzy Sets
Information-Based Characteristics of Fuzzy Sets
Matching Fuzzy Sets
Numerical Representation of Fuzzy Sets
Rough Sets
Rough Sets and Fuzzy Sets
Shadowed Sets
The Frame of Cognition
Probability and Fuzzy Sets
Hybrid Fuzzy-Probabilistic Models of Uncertainty
Conclusions
Problems
References
Chapter 4. Computations with Fuzzy Sets
Introductory Remarks
The Extension Principle
Fuzzy Numbers
Fuzzy Rule-Based Computing
Fuzzy Controller and Fuzzy Control
Rule-Based Systems with Nonmonotonic Operations
Conclusions
Problems
References
Chapter 5. Evolutionary Computing
Introduction
Gradient-Based and Probabilistic Optimization as Examples of Single-Point Search Techniques
Genetic Algorithms - Fundamentals and a Basic Algorithm
Schemata Theorem - A Conceptual Backbone of GAs
From Search Space to GA Search Space
Exploration and Exploitation of the Search Space
Experimental Studies
Classes of Evolutionary Computation
Conclusions
Problems
References
Chapter 6. Fuzzy Neural Systems
Introduction
Neurocomputing in Fuzzy Set Technology
Fuzzy Sets in the Technology of Neurocomputing
Fuzzy Sets in the Preprocessing and Enhancements of Training Data
Uncertainty Representation in Neural Networks
Neural Calibration of Membership Functions
Knowledge-Based Learning Schemes
Linguistic Interpretation of Neural Networks
Hybrid Fuzzy Neural Computing Structures
Conclusions
Problems
References
Chapter 7. Fuzzy Neural Networks
Logic-Based Neurons
Logic Neurons and Fuzzy Neural Networks with Feedback
Referential Logic-Based Neurons
Learning in Fuzzy Neural Networks
Case Studies
Conclusions
Problems
References
Chapter 8. CI Systems
Introduction
Fuzzy Encoding in Evolutionary Computing
Fuzzy Crossover Operations
Fuzzy Metarules in Genetic Computing
Relational Structures and Their Optimization
The Satisfiability Problem
Evolutionary Rule-Based Modeling of Analytical Relationships
Genetic Optimization of Neural Networks
Genetic Optimization of Rule-Based Systems
Conclusions
Problems
References
Index