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.

Autorentext

Nazmul Siddique is a lecturer in the School of Computing and Intelligent Systems at the University of Ulster. He has published over 120 scientific research papers in journals and conferences including seven book chapters and two books. He is a senior member of the IEEE and has been involved in organising many international conferences. He is on the editorial board of the International Journal of Neural Systems, International Journal of Automation and Control Engineering, Journal of Behavioural Robotics, and Engineering Letters.

Hojjat Adeli is the holder of Abba G. Lichtenstein Professorship at The Ohio State University (OSU). He is the Editor-in-Chief of three journals: Computer-Aided Civil and Infrastructure Engineering, Integrated Computer-Aided Engineering, and International Journal of Neural Systems. He has authored over 500 publications including 14 books and has won numerous awards. He is a Distinguished Member of ASCE, a Fellow of AAAS and IEEE. In April 2010 he was profiled as an engineering legend in the journal Leadership and Management in Engineering.

Zusammenfassung

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.

Inhalt

Foreword xiii

Preface xv

Acknowledgements xix

1 Introduction to Computational Intelligence 1

1.1 Computational Intelligence 1

1.2 Paradigms of Computational Intelligence 2

1.3 Approaches to Computational Intelligence 3

1.4 Synergies of Computational Intelligence Techniques 11

1.5 Applications of Computational Intelligence 12

1.6 Grand Challenges of Computational Intelligence 13

1.7 Overview of the Book 13

1.8 MATLAB R _ Basics 14

References 15

2 Introduction to Fuzzy Logic 19

2.1 Introduction 19

2.2 Fuzzy Logic 20

2.3 Fuzzy Sets 21

2.4 Membership Functions 22

2.5 Features of MFs 27

2.6 Operations on Fuzzy Sets 29

2.7 Linguistic Variables 33

2.8 Linguistic Hedges 35

2.9 Fuzzy Relations 37

2.10 Fuzzy IfThen Rules 39

2.11 Fuzzification 43

2.12 Defuzzification 44

2.13 Inference Mechanism 48

2.14 Worked Examples 54

2.15 MATLAB R _ Programs 61

References 61

3 Fuzzy Systems and Applications 65

3.1 Introduction 65

3.2 Fuzzy System 66

3.3 Fuzzy Modelling 67

3.4 Fuzzy Control 75

3.5 Design of Fuzzy Controller 81

3.6 Modular Fuzzy Controller 97

3.7 MATLAB R _ Programs 99

References 100

4 Neural Networks 103

4.1 Introduction 103

4.2 Artificial Neuron Model 106

4.3 Activation Functions 107

4.4 Network Architecture 108

4.5 Learning in Neural Networks 124

4.6 Recurrent Neural Networks 149

4.7 MATLAB R _ Programs 155

References 156

5 Neural Systems and Applications 159

5.1 Introduction 159

5.2 System Identification and Control 160

5.3 Neural Networks for Control 163

5.4 MATLAB R _ Programs 179

References 180

6 Evolutionary Computing 183

6.1 Introduction 183

6.2 Evolutionary Computing 183

6.3 Terminologies of Evolutionary Computing 185

6.4 Genetic Operators 194

6.5 Performance Measures of EA 208

6.6 Evolutionary Algorithms 209

6.7 MATLAB R _ Programs 234

References 235

7 Evolutionary Systems 239

7.1 Introduction 239

7.2 Multi-objective Optimization 243

7.3 Co-evolution 250

7.4 Parallel Evolutionary Algorithm 256

References 262

8 Evolutionary Fuzzy Systems 265

8.1 Introduction 265

8.2 Evolutionary Adaptive Fuzzy Systems 267

8.3 Objective Functions and Evaluation 287

8.4 Fuzzy Adaptive Evolutionary Algorithms 290

References 303

9 Evolutionary Neural Networks 307

9.1 Introduction 307

9.2 Supportive Combinations 309

9.3 Collaborative Combinations 318

9.4 Amalgamated Combination 343

9.5 Competing Conventions 345

References 351

10 Neural Fuzzy Systems 357

10.1 Introduction 357

10.2 Combination of Neural and Fuzzy Systems 359

10.3 Cooperative Neuro-Fuzzy Systems 360

10.4 Concurrent Neuro-Fuzzy Systems 369

10.5 Hybrid Neuro-Fuzzy Systems 369

10.6 Adaptive Neuro-Fuzzy System 404

10.7 Fuzzy Neurons 409

10.8 MATLAB R _ Programs 411

References 412

Appendix A: MATLAB R _ Basics 415

Appendix B: MATLAB R _ Programs for Fuzzy Logic 433 Appendix C: MATLAB R _ **Programs for Fuzz...