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Produktbild: Fundamentals of Computational Intelligence

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

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.07.2016

Verlag

Wiley

Seitenzahl

378

Maße (L/B/H)

24/16,1/2,5 cm

Gewicht

736 g

Sprache

Englisch

ISBN

978-1-119-21434-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.07.2016

Verlag

Wiley

Seitenzahl

378

Maße (L/B/H)

24/16,1/2,5 cm

Gewicht

736 g

Sprache

Englisch

ISBN

978-1-119-21434-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Fundamentals of Computational Intelligence

  • 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