Computational Intelligence Paradigms: Theory & Applications using MATLAB, 1st Edition (Hardback) book cover

Computational Intelligence Paradigms

Theory & Applications using MATLAB, 1st Edition

By S. Sumathi, Surekha Paneerselvam

CRC Press

851 pages | 202 B/W Illus.

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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.

Table of Contents

Computational Intelligence (CI)


Primary Classes of Problems for CI Techniques

Neural Networks

Fuzzy Systems

Evolutionary Computing

Swarm Intelligence

Other Paradigms

Hybrid Approaches

Relationship with Other Paradigms

Challenges to CI

Artificial Neural Networks with MATLAB


A Brief History of Neural Networks

Artificial Neural Networks

Neural Network Components

Artificial Neural Network Architectures and Algorithms


Layered Architecture

Prediction Networks

Classification and Data Association Neural Networks


Neural Networks Based on Classification

Data Association Networks

Data Conceptualization Networks

Applications Areas of Association Neural Networks

MATLAB Programs to Implement Neural Networks

Coin Detection: Using Euclidean Distance (Hamming Net)

Learning Vector Quantization (LVQ)

Character Recognition Using Kohonen SOM Network

The Hopfield Network as an Associative Memory

Generalized Delta Learning Rule and Back Propagation of Errors for a Multilayer Network

Classification of Heart Disease Using LVQ

Neural Network Using MATLAB Simulink

MATLAB-Based Fuzzy Systems


Imprecision and Uncertainty

Crisp and Fuzzy Logic

Fuzzy Sets


Membership Functions


Linguistic Variables

Operations on Fuzzy Sets

Fuzzy Arithmetic

Fuzzy Relations

Fuzzy Composition

Fuzzy Inference and Expert Systems


Fuzzy Rules

Fuzzy Expert System Model

Fuzzy Inference Methods

Fuzzy Inference Systems in MATLAB

Fuzzy Automata and Languages

Fuzzy Control

MATLAB Illustrations on Fuzzy Systems

Application of Fuzzy Controller Using MATLAB: Fuzzy Washing Machine

Fuzzy Control System for a Tanker Ship

Approximation of Any Function Using Fuzzy Logic

Building Fuzzy Simulink Models

Neuro-Fuzzy Modeling


Cooperative and Concurrent Neuro-Fuzzy Systems

Fused Neuro Fuzzy Systems

Hybrid Neuro-Fuzzy Model: ANFIS

Classification and Regression Trees

Data Clustering Algorithms

Neuro-Fuzzy Modeling Using MATLAB

Fuzzy Art Map

Fuzzy C-Means Clustering: Comparative Case Study

K-Means Clustering

Neuro-Fuzzy System Using Simulink

Neuro-Fuzzy System Using Takagi-Sugeno and ANFIS GUI of MATLAB

Evolutionary Computation Paradigms


Evolutionary Computation

Brief History of Evolutionary Computation

Biological and Artificial Evolution

Flow Diagram of a Typical Evolutionary Algorithm

Models of Evolutionary Computation

Evolutionary Algorithms

Evolutionary Programming

Evolutionary Strategies

Advantages and Disadvantages of Evolutionary Computation

Evolutionary Algorithms Implemented Using MATLAB

Design of a Proportional-Derivative Controller Using Evolutionary Algorithm for Tanker Ship Heading Regulation

Maximizing the Given 1-D Function with the Boundaries Using Evolutionary Algorithm

Multi-Objective Optimization Using Evolutionary Algorithm

Evolutionary Strategy for Nonlinear Function Minimization

MATLAB-Based Genetic Algorithm (GA)


Encoding and Optimization Problems

Historical Overview of GA

GA Description

Role of GAs

Solution Representation for GAs

Parameters of GA

Schema Theorem and Theoretical Background

Crossover Operators and Schemata

Genotype and Fitness

Advanced Techniques and Operators of GA

GA versus Traditional Search and Optimization Methods

Benefits of GA

MATLAB Programs on GA

Genetic Programming with MATLAB


Growth of Genetic Programming

The LISP Programming Language

Functionality of Genetic Programming

Genetic Programming in Machine Learning

Elementary Steps of Genetic Programming

Flow Chart of Genetic Programming

Benefits of Genetic Programming

MATLAB Examples Using Genetic Programming

MATLAB-Based Swarm Intelligence (SI)

Introduction to Swarms

Biological Background

Swarm Robots

Stability of Swarms


Particle Swarm Optimization (PSO)

Extended Models of PSO

Ant Colony Optimization

Studies and Applications of SI

MATLAB Examples of SI

Appendix A: Glossary of Terms

Appendix B: List of Abbreviations

Appendix C: MATLAB Toolboxes Based on CI

Appendix D: Emerging Software Packages

Appendix E: Research Projects


A Summary and Review Questions appear at the end of each chapter.

About the Authors

S. Sumathi is an assistant professor in the Department of Electrical and Electronics Engineering at PSG College of Technology, Coimbatore, India. Her research interests include neural networks, fuzzy systems, genetic algorithms, pattern recognition and classification, data warehousing and mining, operating systems, and parallel computing.

Surekha Paneerselvam is a lecturer in the Department of Electronics and Communication Engineering at Adhiyamaan College of Engineering, Hosur, India. Her research interests include robotics, virtual instrumentation, mobile communication, and computational intelligence.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Programming / Algorithms
TECHNOLOGY & ENGINEERING / Electronics / General