Computational Intelligence Paradigms: Theory & Applications using MATLAB
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.
Table of Contents
Computational Intelligence (CI)
Introduction
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
Introduction
A Brief History of Neural Networks
Artificial Neural Networks
Neural Network Components
Artificial Neural Network Architectures and Algorithms
Introduction
Layered Architecture
Prediction Networks
Classification and Data Association Neural Networks
Introduction
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
Introduction
Imprecision and Uncertainty
Crisp and Fuzzy Logic
Fuzzy Sets
Universe
Membership Functions
Singletons
Linguistic Variables
Operations on Fuzzy Sets
Fuzzy Arithmetic
Fuzzy Relations
Fuzzy Composition
Fuzzy Inference and Expert Systems
Introduction
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
Introduction
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
Introduction
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)
Introduction
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
Introduction
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
SI
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
Bibliography
A Summary and Review Questions appear at the end of each chapter.
Author Bio(s)
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.
