Foreword and Preface
- Foreword by Lotfi Zadeh xv
- Preface xix
- Audience xix
- Organization xx
- Features xxii
- Obtaining the Example Programs xxiii
- Acknowledgments xxiv
- How to Contact Us xxvi
Overview
- Chapter 1: Introduction to Neuro-Fuzzy and Soft Computing 1
- 1.1 Introduction 1
- 1.2 Soft Computing Constituents and Conventional Artificial Intelligence 1
- 1.3 Neuro-Fuzzy and Soft Computing Characteristics 7
Part I: Fuzzy Set Theory
- Chapter 2: Fuzzy Sets 13
- 2.1 Introduction 13
- 2.2 Basic Definitions and Terminology 14
- 2.3 Set-theoretic Operations 21
- 2.4 MF Formulation and Parameterization 24
- 2.5 More on Fuzzy Union, Intersection, and Complement* 35
- 2.6 Summary 42
- Exercises 42
- Chapter 3: Fuzzy Rules and Fuzzy Reasoning 47
- 3.1 Introduction 47
- 3.2 Extension Principle and Fuzzy Relations 47
- 3.3 Fuzzy If-Then Rules 54
- 3.4 Fuzzy Reasoning 62
- 3.5 Summary 70
- Exercises 70
- Chapter 4: Fuzzy Inference Systems 73
- 4.1 Introduction 73
- 4.2 Mamdani Fuzzy Models 74
- 4.3 Sugeno Fuzzy Models 81
- 4.4 Tsukamoto Fuzzy Models 84
- 4.5 Other Considerations 85
- 4.6 Summary 89
- Exercises 90
Part II: Regression and Optimization
- Chapter 5: Least-Squares Methods for System Identification 95
- 5.1 System Identification: An Introduction 95
- 5.2 Basics of Matrix Manipulation and Calculus 97
- 5.3 Least-Squares Estimator 104
- 5.4 Geometric Interpretation of LSE 110
- 5.5 Recursive Least-Squares Estimator 113
- 5.6 Recursive LSE for Time-Varying Systems* 116
- 5.7 Statistical Properties and the Maximum Likelihood Estimator* 118
- 5.8 LSE for Nonlinear Models 122
- 5.9 Summary 125
- Exercises 126
- Chapter 6: Derivative-based Optimization 129
- 6.1 Introduction 129
- 6.2 Descent Methods 129
- 6.3 The Method of Steepest Descent 133
- 6.4 Newton's Methods 134
- 6.5 Step Size Determination 141
- 6.6 Conjugate Gradient Methods* 148
- 6.7 Analysis of Quadratic Case 154
- 6.8 Nonlinear Least-squares Problems 160
- 6.9 Incorporation of Stochastic Mechanisms 166
- 6.10 Summary 168
- Exercises 168
- Chapter 7: Derivative-Free Optimization 173
- 7.1 Introduction 173
- 7.2 Genetic Algorithms 175
- 7.3 Simulated Annealing 181
- 7.4 Random Search 186
- 7.5 Downhill Simplex Search 189
- 7.6 Summary 193
- Exercises 194
Part III: Neural Networks
- Chapter 8: Adaptive Networks 199
- 8.1 Introduction 199
- 8.2 Architecture 200
- 8.3 Backpropagation for Feedforward Networks 205
- 8.4 Extended Backpropagation for Recurrent Networks 210
- 8.5 Hybrid Learning Rule: Combining Steepest Descent and LSE 219
- 8.6 Summary 223
- Exercises 223
- Chapter 9: Supervised Learning Neural Networks 226
- 9.1 Introduction 226
- 9.2 Perceptrons 227
- 9.3 Adaline 230
- 9.4 Backpropagation Multilayer Perceptrons 233
- 9.5 Radial Basis Function Networks 238
- 9.6 Modular Networks 246
- 9.7 Summary 250
- Exercises 251
- Chapter 10: Learning from Reinforcement 258
- 10.1 Introduction 258
- 10.2 Failure Is the Surest Path to Success 259
- 10.3 Temporal Difference Learning 264
- 10.4 The Art of Dynamic Programming 270
- 10.5 Adaptive Heuristic Critic 273
- 10.6 Q-learning 278
- 10.7 A Cost Path Problem 281
- 10.8 World Modeling* 288
- 10.9 Other Network Configurations* 290
- 10.10 Reinforcement Learning by Evolutionary Computation* 292
- 10.11 Summary 293
- Exercises 294
- Chapter 11: Unsupervised Learning and Other Neural Networks 301
- 11.1 Introduction 301
- 11.2 Competitive Learning Networks 302
- 11.3 Kohonen Self-Organizing Networks 305
- 11.4 Learning Vector Quantization 308
- 11.5 Hebbian Learning 310
- 11.6 Principal Component Networks 312
- 11.7 The Hopfield Network 316
- 11.8 Summary 327
- Exercises 328
Part IV: Neuro-Fuzzy Modeling
- Chapter 12: ANFIS: Adaptive Neuro-Fuzzy Inference Systems 335
- 12.1 Introduction 335
- 12.2 ANFIS Architecture 336
- 12.3 Hybrid Learning Algorithm 340
- 12.4 Learning Methods that Cross-fertilize ANFIS and RBFN 341
- 12.5 ANFIS as a Universal Approximator* 342
- 12.6 Simulation Examples 345
- 12.7 Extensions and Advanced Topics 360
- Exercises 363
- Chapter 13: Coactive Neuro-Fuzzy Modeling: Towards Generalized ANFIS 369
- 13.1 Introduction 369
- 13.2 Framework 370
- 13.3 Neuron Functions for Adaptive Networks 372
- 13.4 Neuro-Fuzzy Spectrum 382
- 13.5 Analysis of Adaptive Learning Capability 385
- 13.6 Summary 393
- Exercises 395
Part V: Advanced Neuro-Fuzzy Modeling
- Chapter 14: Classification and Regression Trees 403
- 14.1 Introduction 403
- 14.2 Decision Trees 404
- 14.3 CART Algorithm for Tree Induction 406
- 14.4 Using CART for Structure Identification in ANFIS 416
- 14.5 Summary 421
- Exercises 421
- Chapter 15: Data Clustering Algorithms 423
- 15.1 Introduction 423
- 15.2 K-Means Clustering 424
- 15.3 Fuzzy C-Means Clustering 425
- 15.4 Mountain Clustering Method 427
- 15.5 Subtractive Clustering 431
- 15.6 Summary 432
- Exercises 432
- Chapter 16: Rulebase Structure Identification 434
- 16.1 Introduction 434
- 16.2 Input Selection 435
- 16.3 Input Space Partitioning 436
- 16.4 Rulebase Organization 441
- 16.5 Focus Set--Based Rule Combination 446
- 16.6 Summary 447
- Exercises 448
Part VI: Neuro-Fuzzy Control
- Chapter 17: Neuro-Fuzzy Control I 453
- 17.1 Introduction 453
- 17.2 Feedback Control Systems and Neuro-Fuzzy Control: An Overview 454
- 17.3 Expert Control: Mimicking An Expert 458
- 17.4 Inverse Learning 460
- 17.5 Specialized Learning 465
- 17.6 Backpropagation Through Time and Real-Time Recurrent Learning 469
- 17.7 Summary 476
- Exercises 477
- Chapter 18: Neuro-Fuzzy Control II 480
- 18.1 Introduction 480
- 18.2 Reinforcement Learning Control 480
- 18.3 Gradient-Free Optimization 483
- 18.4 Gain Scheduling 489
- 18.5 Feedback Linearization and Sliding Control 493
- 18.6 Summary 496
- Exercises 497
Part VII: Advanced Applications
- Chapter 19: ANFIS Applications 503
- 19.1 Introduction 503
- 19.2 Printed Character Recognition 503
- 19.3 Inverse Kinematics Problems 507
- 19.4 Automobile MPG Prediction 510
- 19.5 Nonlinear System Identification 514
- 19.6 Channel Equalization 516
- 19.7 Adaptive Noise Cancellation 523
- Chapter 20: Fuzzy-Filtered Neural Networks 535
- 20.1 Introduction 535
- 20.2 Fuzzy-Filtered Neural Networks 536
- 20.3 Application 1: Plasma Spectrum Analysis 538
- 20.4 Application 2: Hand-Written Numeral Recognition 540
- 20.5 Genetic Algorithm--based Fuzzy Filters 543
- 20.6 Summary 549
- Chapter 21: Fuzzy Sets and Genetic Algorithms in Game Playing 551
- 21.1 Introduction 551
- 21.2 Variants of Genetic Algorithms 551
- 21.3 Using Genetic Algorithms in Game Playing 553
- 21.4 Simulation Results of the Basic Model 556
- 21.5 Using Fuzzily Characterized Features 559
- 21.6 Using Polyploid GA in Game Playing 560
- 21.7 Summary 564
- Chapter 22: Soft Computing for Color Recipe Prediction 568
- 22.1 Introduction 568
- 22.2 Color Recipe Prediction 569
- 22.3 Single MLP approaches 569
- 22.4 CANFIS modeling for Color Recipe Prediction 571
- 22.5 Color Paint Manufacturing Intelligence 577
- 22.6 Experimental Evaluation 587
- 22.7 Discussion 589
- 22.8 Concluding Remarks and Future Directions 591
- Appendix A: Hints to Selected Exercises 595
- Appendix B: List of Internet Resources 598
- Appendix C: List of MATLAB Programs 601
- Appendix D: List of Acronyms 604
- Index 607
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