Offering a wide range of programming examples implemented in MATLAB(R), Computational Intelligence Paradigms: Theory and Applications Using MATLAB(R) 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.
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.