Computational Intelligence Paradigms:

Theory & Applications using MATLAB

Published: January 5, 2010 by CRC Press
Content:851 Pages | 202 B/W Illustrations
Author(s):S. Sumathi, Surekha Paneerselvam

Features

• Includes detailed descriptions of CI paradigms
• Provides worked examples of neural networks, fuzzy systems, hybrid neuro-fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence
• Presents MATLAB toolboxes and their functions for neural networks, fuzzy logic, genetic algorithms and programming, evolutionary algorithms, and swarm optimization
• Contains research projects, a survey of emerging commercial software packages, and numerous review questions
  

Summary

Offering a wide range of programming examples implemented in MATLAB®, Computational Intelligence Paradigms: Theory and Applications Using MATLAB® presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research.

The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi–Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.

Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.