Computational Intelligence

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

Computational Intelligence: Synergies of Fuzzy Logic, NeuralNetworks and Evolutionary Computing presents an introduction tosome of the cutting edge technological paradigms under the umbrellaof computational intelligence. Computational intelligence schemesare investigated with the development of a suitable framework forfuzzy logic, neural networks and evolutionary computingneuro-fuzzy systems, evolutionary-fuzzy systems and evolutionaryneural systems. Applications to linear and non-linear systems arediscussed with examples.Key features:* Covers all the aspects of fuzzy, neural and evolutionaryapproaches with worked out examples, MATLAB® exercises andapplications in each chapter* Presents the synergies of technologies of computationalintelligence such as evolutionary fuzzy neural fuzzy andevolutionary neural systems* Considers real world problems in the domain of systemsmodelling, control and optimization* Contains a foreword written by Lotfi ZadehComputational Intelligence: Synergies of Fuzzy Logic, NeuralNetworks and Evolutionary Computing is an ideal text for finalyear undergraduate, postgraduate and research students inelectrical, control, computer, industrial and manufacturingengineering.

Foreword xiiiPreface xvAcknowledgements xix1 Introduction to Computational Intelligence 11.1 Computational Intelligence 11.2 Paradigms of Computational Intelligence 21.3 Approaches to Computational Intelligence 31.4 Synergies of Computational Intelligence Techniques 111.5 Applications of Computational Intelligence 121.6 Grand Challenges of Computational Intelligence 131.7 Overview of the Book 131.8 MATLAB R _ Basics 14References 152 Introduction to Fuzzy Logic 192.1 Introduction 192.2 Fuzzy Logic 202.3 Fuzzy Sets 212.4 Membership Functions 222.5 Features of MFs 272.6 Operations on Fuzzy Sets 292.7 Linguistic Variables 332.8 Linguistic Hedges 352.9 Fuzzy Relations 372.10 Fuzzy If-Then Rules 392.11 Fuzzification 432.12 Defuzzification 442.13 Inference Mechanism 482.14 Worked Examples 542.15 MATLAB R _ Programs 61References 613 Fuzzy Systems and Applications 653.1 Introduction 653.2 Fuzzy System 663.3 Fuzzy Modelling 673.4 Fuzzy Control 753.5 Design of Fuzzy Controller 813.6 Modular Fuzzy Controller 973.7 MATLAB R _ Programs 99References 1004 Neural Networks 1034.1 Introduction 1034.2 Artificial Neuron Model 1064.3 Activation Functions 1074.4 Network Architecture 1084.5 Learning in Neural Networks 1244.6 Recurrent Neural Networks 1494.7 MATLAB R _ Programs 155References 1565 Neural Systems and Applications 1595.1 Introduction 1595.2 System Identification and Control 1605.3 Neural Networks for Control 1635.4 MATLAB R _ Programs 179References 1806 Evolutionary Computing 1836.1 Introduction 1836.2 Evolutionary Computing 1836.3 Terminologies of Evolutionary Computing 1856.4 Genetic Operators 1946.5 Performance Measures of EA 2086.6 Evolutionary Algorithms 2096.7 MATLAB R _ Programs 234References 2357 Evolutionary Systems 2397.1 Introduction 2397.2 Multi-objective Optimization 2437.3 Co-evolution 2507.4 Parallel Evolutionary Algorithm 256References 2628 Evolutionary Fuzzy Systems 2658.1 Introduction 2658.2 Evolutionary Adaptive Fuzzy Systems 2678.3 Objective Functions and Evaluation 2878.4 Fuzzy Adaptive Evolutionary Algorithms 290References 3039 Evolutionary Neural Networks 3079.1 Introduction 3079.2 Supportive Combinations 3099.3 Collaborative Combinations 3189.4 Amalgamated Combination 3439.5 Competing Conventions 345References 35110 Neural Fuzzy Systems 35710.1 Introduction 35710.2 Combination of Neural and Fuzzy Systems 35910.3 Cooperative Neuro-Fuzzy Systems 36010.4 Concurrent Neuro-Fuzzy Systems 36910.5 Hybrid Neuro-Fuzzy Systems 36910.6 Adaptive Neuro-Fuzzy System 40410.7 Fuzzy Neurons 40910.8 MATLAB R _ Programs 411References 412Appendix A: MATLAB R _ Basics 415Appendix B: MATLAB R _ Programs for Fuzzy Logic433Appendix C: MATLAB R _ Programs for FuzzySystems 443Appendix D: MATLAB R _ Programs for NeuralSystems 461Appendix E: MATLAB R _ Programs for NeuralControl Design 473Appendix F: MATLAB R _ Programs forEvolutionary Algorithms 489Appendix G: MATLAB R _ Programs for Neuro-FuzzySystems 497Index 507