Fundamentals of Computational Intelligence Neural Networks, Fuzzy Systems, and Evolutionary Computation

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another 

This book covers the three fundamental topics that form the basis of computational intelligence:  neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.

• Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks
• Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals
• Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence
• Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems

Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

James Keller holds the University of Missouri Curators' Professorship in the Electrical and Computer Engineering and Computer Science Departments on the Columbia Campus, and is the R.L. Tatum Professor in the College of Engineering. Dr. Keller is a Life Fellow of the IEEE, a Fellow of the International Fuzzy Systems Association, and a former president of the North American Fuzzy Information Processing Society.

Derong Liu is a Professor of Electrical and Computer Engineering at the University of Illinois at Chicago, USA, and a Professor of Automation and Electrical Engineering at the University of Science and Technology Beijing, China. Dr. Liu is a Fellow of the IEEE and a Fellow of the International Neural Network Society. He has published 17 books, including Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (2012, Wiley-IEEE Press). He is the Editor-in-Chief of Artificial Intelligence Review, and he served as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2010-2015).

David Fogel is the President of Natural Selection, Inc., CEO of Natural Selection Financial, Inc., a Fellow of the IEEE, and the series editor for the Wiley-IEEE Press Series on Computational Intelligence. Dr. Fogel has 30 years of experience pioneering contributions in the field of computational intelligence, and is co-inventor of the EffectCheck® sentiment analysis system. He has written several books including Evolutionary Computation: The Fossil Record (1998) and Evolutionary Computation Toward a New Philosophy of Machine Intelligence, 3rd Edition (2005), both published by the Wiley-IEEE Press.

Acknowledgments xi

1. Introduction to Computational Intelligence 1

1.1 Welcome to Computational Intelligence 1

1.2 What Makes This Book Special 1

1.3 What This Book Covers 2

1.4 How to Use This Book 2

1.5 Final Thoughts Before You Get Started 3

PART I NEURAL NETWORKS 5

2. Introduction and Single-Layer Neural Networks 7

2.1 Short History of Neural Networks 9

2.2 Rosenblatt’s Neuron 10

2.3 Perceptron Training Algorithm 13

2.4 The Perceptron Convergence Theorem 23

2.5 Computer Experiment Using Perceptrons 25

2.6 Activation Functions 28

Exercises 30

3. Multilayer Neural Networks and Backpropagation 35

3.1 Universal Approximation Theory 35

3.2 The Backpropagation Training Algorithm 37

3.3 Batch Learning and Online Learning 45

3.4 Cross-Validation and Generalization 47

3.5 Computer Experiment Using Backpropagation 53

Exercises 56

4. Radial-Basis Function Networks 61

4.1 Radial-Basis Functions 61

4.2 The Interpolation Problem 62

4.3 Training Algorithms For Radial-Basis Function Networks 64

4.4 Universal Approximation 69

4.5 Kernel Regression 70

Exercises 75

5. Recurrent Neural Networks 77

5.1 The Hopfield Network 77

5.2 The Grossberg Network 81

5.3 Cellular Neural Networks 88

5.4 Neurodynamics and Optimization 91

5.5 Stability Analysis of Recurrent Neural Networks 93

Exercises 99

PART II FUZZY SET THEORY AND FUZZY LOGIC 101

6. Basic Fuzzy Set Theory 103

6.1 Introduction 103

6.2 A Brief History 107

6.3 Fuzzy Membership Functions and Operators 108

6.4 Alpha-Cuts, The Decomposition Theorem, and The Extension Principle 117

6.5 Compensatory Operators 120

6.6 Conclusions 124

Exercises 124

7. Fuzzy Relations and Fuzzy Logic Inference 127

7.1 Introduction 127

7.2 Fuzzy Relations and Propositions 128

7.3 Fuzzy Logic Inference 131

7.4 Fuzzy Logic For Real-Valued Inputs 135

7.5 Where Do The Rules Come From? 138

7.6 Chapter Summary 142

Exercises 143

8. Fuzzy Clustering and Classification 147

8.1 Introduction to Fuzzy Clustering 147

8.2 Fuzzy c-Means 155

8.3 An Extension of The Fuzzy c-Means 167

8.4 Possibilistic c-Means 169

8.5 Fuzzy Classifiers: Fuzzy k-Nearest Neighbors 174

8.6 Chapter Summary 179

Exercises 180

9. Fuzzy Measures and Fuzzy Integrals 183

9.1 Fuzzy Measures 183

9.2 Fuzzy Integrals 188

9.3 Training The Fuzzy Integrals 191

9.4 Summary and Final Thoughts 203

Exercises 203

PART III EVOLUTIONARY COMPUTATION 207

10. Evolutionary Computation 209

10.1 Basic Ideas and Fundamentals 209

10.2 Evolutionary Algorithms: Generate and Test 216

10.3 Representation, Search, and Selection Operators 221

10.4 Major Research and Application Areas 223

10.5 Summary 225

Exercises 225

11. Evolutionary Optimization 227

11.1 Global Numerical Optimization 229

11.2 Combinatorial Optimization 233

11.3 Some Mathematical Considerations 238

11.4 Constraint Handling 255

11.5 Self-Adaptation 258

11.6 Summary 264

Exercises 265

12. Evolutionary Learning and Problem Solving 269

12.1 Evolving Parameters of A Regression Equation 270

12.2 Evolving The Structure and Parameters of Input–Output Systems 274

12.3 Evolving Clusters 292

12.4 Evolutionary Classification Models 298

12.5 Evolutionary Control Systems 307

12.6 Evolutionary Games 314

12.7 Summary 320

Exercises 321

13. Collective Intelligence and Other Extensions of Evolutionary Computation 323

13.1 Particle Swarm Optimization 323

13.2 Differential Evolution 326

13.3 Ant Colony Optimization 329

13.4 Evolvable Hardware 331

13.5 Interactive Evolutionary Computation 333

13.6 Multicriteria Evolutionary Optimization 335

13.7 Summary 340

Exercises 340

References 343

Index 361

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