Soft Computing Is A Branch Of Computer Science That Deals With A Family Of Methods That Imitate Human Intelligence. This Is Done With The Goal Of Creating Tools That Will Contain Some Human-like Capabilities (such As Learning, Reasoning And Decision-making). This Book Covers The Entire Gamut Of Soft Computing, Including Fuzzy Logic, Rough Sets, Artificial Neural Networks, And Various Evolutionary Algorithms. It Offers A Learner-centric Approach Where Each New Concept Is Introduced With Carefully Designed Examples/instances To Train The Learner.

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nexusstc/Introduction to soft computing: neuro-fuzzy and genetic algorithms/b825e0b08cecbcd67361a5719692f743.pdf

zlib/Computers/Chakraborty, Udit; Roy, Samir/Introduction to soft computing: neuro-fuzzy and genetic algorithms_10999955.pdf

SOFT COMPUTING : neuro-fuzzy and genetic algorithms;neuro-fuzzy and genetic algorithms

Samir Roy, Udit Chakraborty

SAMIR;CHAKRABORTY, UDIT ROY

BeSpokePG004

Dorling Kindersley (India)

Pearson Education, Inc

Ramesh Subbarao

Place of publication not identified, 2013

First edition, Delhi, 2013

UPPER SADDLE RIVER, 2013

First Edition, PS, 2013

India, India

1st, 2013

lg2859685

producers:
Acrobat Distiller 8.0.0 (Windows)

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Includes bibliographic references.

Cover......Page 1
Contents......Page 8
Preface......Page 16
Acknowledgements......Page 18
About the Authors......Page 20
1.1 What is Soft Computing?......Page 22
1.2 Fuzzy Systems......Page 26
1.4 Artificial Neural Networks......Page 27
1.5 Evolutionary Search Strategies......Page 28
Test Your Knowledge......Page 29
Bibliography and Historical Notes......Page 30
2.1 Crisp Sets: A Review......Page 32
2.1.1 Basic Concepts......Page 33
2.1.2 Operations on Sets......Page 34
2.1.3 Properties of Sets......Page 36
2.2 Fuzzy Sets......Page 37
2.2.2 Set Membership......Page 38
2.2.3 Fuzzy Sets......Page 40
2.2.5 Features of Fuzzy Sets......Page 42
2.3.1 Some Popular Fuzzy Membership Functions......Page 43
2.3.2 Transformations......Page 45
2.3.3 Linguistic Variables......Page 47
2.4 Operations on Fuzzy Sets......Page 48
2.5.1 Crisp Relations......Page 52
2.5.2 Fuzzy Relations......Page 55
2.5.3 Operations on Fuzzy Relations......Page 57
2.6.1 Preliminaries......Page 59
2.6.2 The Extension Principle......Page 62
Chapter Summary......Page 66
Solved Problems......Page 67
Test Your Knowledge......Page 77
Exercises......Page 79
Bibliography and Historical Notes......Page 81
Chapter 3:Fuzzy Logic......Page 84
3.1.1 Propositional Logic......Page 85
3.1.2 Predicate Logic......Page 90
3.1.3 Rules of Inference......Page 98
3.2.1 Fuzzy Truth Values......Page 102
3.3 Fuzzy Truth in Terms of Fuzzy Sets......Page 104
3.4 Fuzzy Rules......Page 105
3.4.1 Fuzzy If-Then......Page 106
3.4.2 Fuzzy If-Then-Else......Page 107
3.5.2 Generalized Modus Ponens......Page 109
Chapter Summary......Page 112
Solved Problems......Page 114
Test Your Knowledge......Page 125
Exercises......Page 128
Bibliography and Historical Notes......Page 130
Introduction......Page 132
4.2 Fuzzification of the Input Variables......Page 133
4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules......Page 134
4.4 Evaluation of the Fuzzy Rules......Page 135
4.6 Defuzzification of the Resultant Aggregate Fuzzy Set......Page 136
4.6.1 Centroid Method......Page 137
4.6.3 Mean-of-Maxima (MoM) Method......Page 139
4.7 Fuzzy Controllers......Page 141
4.7.1 Fuzzy Air Conditioner Controller......Page 143
4.7.2 Fuzzy Cruise Controller......Page 148
Chapter Summary......Page 151
Solved Problems......Page 152
Test Your Knowledge......Page 163
Exercises......Page 164
Bibliography and Historical Notes......Page 165
Chapter 5:Rough Sets......Page 166
5.1 Information Systems and Decision Systems......Page 167
5.2 Indiscernibility......Page 169
5.3 Set Approximations......Page 171
5.4 Properties of Rough Sets......Page 173
5.5 Rough Membership......Page 174
5.6 Reducts......Page 175
Application......Page 178
Chapter Summary......Page 182
Solved Problems......Page 183
Test Your Knowledge......Page 189
Exercises......Page 191
Bibliography and Historical Notes......Page 192
Chapter 6:Artificial Neural Networks:Basic Concepts......Page 194
6.1 Introduction......Page 195
6.1.1 The Biological Neuron......Page 196
6.1.2 The Artificial Neuron......Page 197
6.1.3 Characteristics of the Brain......Page 199
6.2.1 Pattern Classification......Page 200
6.2.2 Pattern Association......Page 202
6.3 The McCulloch–Pitts Neural Model......Page 205
6.4.1 The Structure......Page 210
6.4.2 Linear Separability......Page 212
6.4.3 The XOR Problem......Page 214
6.5 Neural Network Architectures......Page 215
6.5.1 Single Layer Feed Forward ANNs......Page 216
6.5.2 Multilayer Feed Forward ANNs......Page 217
6.5.3 Competitive Network......Page 218
6.6.1 Identity Function......Page 219
6.6.2 Step Function......Page 220
6.6.3 The Sigmoid Function......Page 221
6.6.4 Hyperbolic Tangent Function......Page 222
6.7 Learning by Neural Nets......Page 223
6.7.1 Supervised Learning......Page 224
6.7.2 Unsupervised Learning......Page 232
Chapter Summary......Page 241
Solved Problems......Page 242
Test Your Knowledge......Page 247
Exercises......Page 249
Bibliography and Historical Notes......Page 251
7.1 Hebb Nets......Page 254
7.2 Perceptrons......Page 259
7.3 Adaline......Page 262
7.4 Madaline......Page 264
Solved Problems......Page 272
Test Your Knowledge......Page 278
Bibliography and Historical Notes......Page 279
Chapter 8:Pattern Associators......Page 280
8.1.1 Training......Page 281
8.1.2 Application......Page 282
8.1.3 Elimination of Self-connection......Page 283
8.1.4 Recognition of Noisy Patterns......Page 284
8.1.5 Storage of Multiple Patterns in an Auto-associative Net......Page 285
8.2 Hetero-associative Nets......Page 286
8.2.1 Training......Page 287
8.3 Hopfield Networks......Page 288
8.3.2 Training......Page 289
8.4.1 Architecture......Page 292
8.4.3 Application......Page 293
Chapter Summary......Page 299
Solved Problems......Page 300
Test Your Knowledge......Page 316
Answers......Page 317
Exercises......Page 318
Bibliography and Historical Notes......Page 319
Chapter 9:Competitive Neural Nets......Page 320
9.1.2 Application of Maxnet......Page 321
9.2.1 SOM Architecture......Page 325
9.2.2 Learning by Kohonen’s SOM......Page 327
9.2.3 Application......Page 328
9.3.1 LVQ Learning......Page 332
9.3.2 Application......Page 334
9.4 Adaptive Resonance Theory (ART)......Page 339
9.4.2 Features of ART Nets......Page 340
9.4.3 Art 1......Page 341
Chapter Summary......Page 359
Solved Problems......Page 360
Test Your Knowledge......Page 386
Exercises......Page 388
Bibliography and Historical Notes......Page 389
10.1 Multi-layer Feedforward Net......Page 392
10.1.2 Notational Convention......Page 393
10.1.3 Activation Functions......Page 394
10.2 The Generalized Delta Rule......Page 396
10.3 The Backpropagation Algorithm......Page 397
10.3.1 Choice of Parameters......Page 400
10.3.2 Application......Page 402
Chapter Summary......Page 403
Solved Problems......Page 404
Test Your Knowledge......Page 412
Exercises......Page 413
Bibliography and Historical Notes......Page 414
Chapter 11:Elementary Search Techniques......Page 416
11.1 State Spaces......Page 417
11.2.1 Basic Graph Search Algorithm......Page 424
11.3 Exhaustive Search......Page 425
11.3.1 Breadth-first Search (BFS)......Page 426
11.3.2 Depth-first Search (DFS)......Page 428
11.3.3 Comparison Between BFS and DFS......Page 431
11.3.4 Depth-first Iterative Deepening......Page 433
11.3.5 Bidirectional Search......Page 434
11.4.1 Best-first Search......Page 437
11.4.3 Hill Climbing......Page 439
11.4.4 The A/A* Algorithms......Page 447
11.4.5 Problem Reduction......Page 458
11.4.6 Means-ends Analysis......Page 467
11.4.7 Mini-Max Search......Page 471
11.4.8 Constraint Satisfaction......Page 486
11.4.9 Measures of Search......Page 497
11.5 Production Systems......Page 498
Chapter Summary......Page 507
Solved Problems......Page 508
Test Your Knowledge......Page 536
Exercises......Page 545
Bibliography and Historical Notes......Page 548
Chapter 12:Advanced Search Strategies......Page 550
12.1.1 Chromosomes......Page 551
12.2 Genetic Algorithms (GAs)......Page 552
12.2.1 Chromosomes......Page 555
12.2.3 Population......Page 558
12.2.4 GA Operators......Page 559
12.2.6 GA Parameters......Page 565
12.2.7 Convergence......Page 566
12.3.1 MOO Problem Formulation......Page 567
12.3.2 The Pareto-optimal Front......Page 568
12.3.3 Pareto-optimal Ranking......Page 570
12.3.4 Multi-objective Fitness......Page 572
12.3.5 Multi-objective GA Process......Page 574
12.4 Simulated Annealing......Page 575
Chapter Summary......Page 576
Solved Problems......Page 577
Test Your Knowledge......Page 582
Bibliography and Historical Notes......Page 584
Chapter 13:Hybrid Systems......Page 586
13.1.1 GA-based Weight Determination of Multi-layerFeed-forward Net......Page 587
13.1.2 Neuro-evolution of Augmenting Topologies (NEAT)......Page 589
13.2 Fuzzy-Neural Systems......Page 595
13.2.1 Fuzzy Neurons......Page 596
13.2.2 Adaptive Neuro-fuzzy Inference System (ANFIS)......Page 598
13.3 Fuzzy-genetic Systems......Page 600
Chapter Summary......Page 602
Test Your Knowledge......Page 603
Bibliography and Historical Notes......Page 604
Index......Page 606

Cover 1
Contents 8
Preface 16
Acknowledgements 18
About the Authors 20
Chapter 1: Introduction 22
1.1 What is Soft Computing? 22
1.2 Fuzzy Systems 26
1.3 Rough Sets 27
1.4 Artificial Neural Networks 27
1.5 Evolutionary Search Strategies 28
Chapter Summary 29
Test Your Knowledge 29
Answers 30
Exercises 30
Bibliography and Historical Notes 30
Chapter 2: Fuzzy Sets 32
2.1 Crisp Sets: A Review 32
2.1.1 Basic Concepts 33
2.1.2 Operations on Sets 34
2.1.3 Properties of Sets 36
2.2 Fuzzy Sets 37
2.2.1 Fuzziness/Vagueness/Inexactness 38
2.2.2 Set Membership 38
2.2.3 Fuzzy Sets 40
2.2.4 Fuzzyness vs. Probability 42
2.2.5 Features of Fuzzy Sets 42
2.3 Fuzzy Membership Functions 43
2.3.1 Some Popular Fuzzy Membership Functions 43
2.3.2 Transformations 45
2.3.3 Linguistic Variables 47
2.4 Operations on Fuzzy Sets 48
2.5 Fuzzy Relations 52
2.5.1 Crisp Relations 52
2.5.2 Fuzzy Relations 55
2.5.3 Operations on Fuzzy Relations 57
2.6 Fuzzy Extension Principle 59
2.6.1 Preliminaries 59
2.6.2 The Extension Principle 62
Chapter Summary 66
Solved Problems 67
Test Your Knowledge 77
Answers 79
Exercises 79
Bibliography and Historical Notes 81
Chapter 3: Fuzzy Logic 84
3.1 Crisp Logic: A Review 85
3.1.1 Propositional Logic 85
3.1.2 Predicate Logic 90
3.1.3 Rules of Inference 98
3.2 Fuzzy Logic Basics 102
3.2.1 Fuzzy Truth Values 102
3.3 Fuzzy Truth in Terms of Fuzzy Sets 104
3.4 Fuzzy Rules 105
3.4.1 Fuzzy If-Then 106
3.4.2 Fuzzy If-Then-Else 107
3.5 Fuzzy Reasoning 109
3.5.1 Fuzzy Quantifiers 109
3.5.2 Generalized Modus Ponens 109
3.5.3 Generalized Modus Tollens 112
Chapter Summary 112
Solved Problems 114
Test Your Knowledge 125
Answers 128
Exercises 128
Bibliography and Historical Notes 130
Chapter 4: Fuzzy Inference Systems 132
Introduction 132
4.2 Fuzzification of the Input Variables 133
4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules 134
4.4 Evaluation of the Fuzzy Rules 135
4.5 Aggregation of Output Fuzzy Sets Across the Rules 136
4.6 Defuzzification of the Resultant Aggregate Fuzzy Set 136
4.6.1 Centroid Method 137
4.6.2 Centre-of-Sums (CoS) Method 139
4.6.3 Mean-of-Maxima (MoM) Method 139
4.7 Fuzzy Controllers 141
4.7.1 Fuzzy Air Conditioner Controller 143
4.7.2 Fuzzy Cruise Controller 148
Chapter Summary 151
Solved Problems 152
Test Your Knowledge 163
Answers 164
Exercises 164
Bibliography and Historical Notes 165
Chapter 5: Rough Sets 166
5.1 Information Systems and Decision Systems 167
5.2 Indiscernibility 169
5.3 Set Approximations 171
5.4 Properties of Rough Sets 173
5.5 Rough Membership 174
5.6 Reducts 175
Application 178
Chapter Summary 182
Solved Problems 183
Test Your Knowledge 189
Answers 191
Exercises 191
Bibliography and Historical Notes 192
Chapter 6: Artificial Neural Networks:Basic Concepts 194
6.1 Introduction 195
6.1.1 The Biological Neuron 196
6.1.2 The Artificial Neuron 197
6.1.3 Characteristics of the Brain 199
6.2 Computation in Terms of Patterns 200
6.2.1 Pattern Classification 200
6.2.2 Pattern Association 202
6.3 The McCulloch–Pitts Neural Model 205
6.4 The Perceptron 210
6.4.1 The Structure 210
6.4.2 Linear Separability 212
6.4.3 The XOR Problem 214
6.5 Neural Network Architectures 215
6.5.1 Single Layer Feed Forward ANNs 216
6.5.2 Multilayer Feed Forward ANNs 217
6.5.3 Competitive Network 218
6.5.4 Recurrent Networks 219
6.6 Activation Functions 219
6.6.1 Identity Function 219
6.6.2 Step Function 220
6.6.3 The Sigmoid Function 221
6.6.4 Hyperbolic Tangent Function 222
6.7 Learning by Neural Nets 223
6.7.1 Supervised Learning 224
6.7.2 Unsupervised Learning 232
Chapter Summary 241
Solved Problems 242
Test Your Knowledge 247
Answers 249
Exercises 249
Bibliography and Historical Notes 251
Chapter 7: Pattern Classifiers 254
7.1 Hebb Nets 254
7.2 Perceptrons 259
7.3 Adaline 262
7.4 Madaline 264
Chapter Summary 272
Solved Problems 272
Test Your Knowledge 278
Answers 279
Exercises 279
Bibliography and Historical Notes 279
Chapter 8: Pattern Associators 280
8.1 Auto-associative Nets 281
8.1.1 Training 281
8.1.2 Application 282
8.1.3 Elimination of Self-connection 283
8.1.4 Recognition of Noisy Patterns 284
8.1.5 Storage of Multiple Patterns in an Auto-associative Net 285
8.2 Hetero-associative Nets 286
8.2.1 Training 287
8.2.2 Application 288
8.3 Hopfield Networks 288
8.3.1 Architecture 289
8.3.2 Training 289
8.4 Bidirectional Associative Memory 292
8.4.1 Architecture 292
8.4.2 Training 293
8.4.3 Application 293
Chapter Summary 299
Solved Problems 300
Test Your Knowledge 316
Answers 317
Exercises 318
Bibliography and Historical Notes 319
Chapter 9: Competitive Neural Nets 320
9.1 The Maxnet 321
9.1.1 Training a MAXNET 321
9.1.2 Application of Maxnet 321
9.2 Kohonen’s Self-organizing Map (SOM) 325
9.2.1 SOM Architecture 325
9.2.2 Learning by Kohonen’s SOM 327
9.2.3 Application 328
9.3 Learning Vector Quantization (LVQ) 332
9.3.1 LVQ Learning 332
9.3.2 Application 334
9.4 Adaptive Resonance Theory (ART) 339
9.4.1 The Stability-Plasticity Dilemma 340
9.4.2 Features of ART Nets 340
9.4.3 Art 1 341
Chapter Summary 359
Solved Problems 360
Test Your Knowledge 386
Answers 388
Exercises 388
Bibliography and Historical Notes 389
Chapter 10: Backpropagation 392
10.1 Multi-layer Feedforward Net 392
10.1.1 Architecture 393
10.1.2 Notational Convention 393
10.1.3 Activation Functions 394
10.2 The Generalized Delta Rule 396
10.3 The Backpropagation Algorithm 397
10.3.1 Choice of Parameters 400
10.3.2 Application 402
Chapter Summary 403
Solved Problems 404
Test Your Knowledge 412
Answers 413
Exercises 413
Bibliography and Historical Notes 414
Chapter 11: Elementary Search Techniques 416
11.1 State Spaces 417
11.2 State Space Search 424
11.2.1 Basic Graph Search Algorithm 424
11.2.2 Informed and Uninformed Search 425
11.3 Exhaustive Search 425
11.3.1 Breadth-first Search (BFS) 426
11.3.2 Depth-first Search (DFS) 428
11.3.3 Comparison Between BFS and DFS 431
11.3.4 Depth-first Iterative Deepening 433
11.3.5 Bidirectional Search 434
11.3.6 Comparison of Basic Uninformed Search Strategies 437
11.4 Heuristic Search 437
11.4.1 Best-first Search 437
11.4.2 Generalized State Space Search 439
11.4.3 Hill Climbing 439
11.4.4 The A/A* Algorithms 447
11.4.5 Problem Reduction 458
11.4.6 Means-ends Analysis 467
11.4.7 Mini-Max Search 471
11.4.8 Constraint Satisfaction 486
11.4.9 Measures of Search 497
11.5 Production Systems 498
Chapter Summary 507
Solved Problems 508
Test Your Knowledge 536
Answers 545
Exercises 545
Bibliography and Historical Notes 548
Chapter 12: Advanced Search Strategies 550
12.1 Natural Evolution: A Brief Review 551
12.1.1 Chromosomes 551
12.1.2 Natural Selection 552
12.1.3 Crossover 552
12.1.4 Mutation 552
12.2 Genetic Algorithms (GAs) 552
12.2.1 Chromosomes 555
12.2.2 Fitness Function 558
12.2.3 Population 558
12.2.4 GA Operators 559
12.2.5 Elitism 565
12.2.6 GA Parameters 565
12.2.7 Convergence 566
12.3 Multi-objective Genetic Algorithms 567
12.3.1 MOO Problem Formulation 567
12.3.2 The Pareto-optimal Front 568
12.3.3 Pareto-optimal Ranking 570
12.3.4 Multi-objective Fitness 572
12.3.5 Multi-objective GA Process 574
12.4 Simulated Annealing 575
Chapter Summary 576
Solved Problems 577
Test Your Knowledge 582
Answers 584
Exercise 584
Bibliography and Historical Notes 584
Chapter 13: Hybrid Systems 586
13.1 Neuro-genetic Systems 587
13.1.1 GA-based Weight Determination of Multi-layerFeed-forward Net 587
13.1.2 Neuro-evolution of Augmenting Topologies (NEAT) 589
13.2 Fuzzy-Neural Systems 595
13.2.1 Fuzzy Neurons 596
13.2.2 Adaptive Neuro-fuzzy Inference System (ANFIS) 598
13.3 Fuzzy-genetic Systems 600
Chapter Summary 602
Test Your Knowledge 603
Answers 604
Bibliography and Historical Notes 604
Index 606

2020-11-29