Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition

By Zheru Chi, Hong Yan, Tuan Pham,
by Zheru Chi (Hong Kong Polytechnic University, Hong Kong), Hong Yan (University of Sydney, Australia), & Tuan Pham (University of Sydney, Australia)

Publisher: World Scientific Pub Co Inc
Number Of Pages: 225
Publication Date: 1996-12
Sales Rank: 573552
ISBN / ASIN: 9810226977
EAN: 9789810226978
Binding: Hardcover
Manufacturer: World Scientific Pub Co Inc
Studio: World Scientific Pub Co Inc

Short Description for Fuzzy Algorithms: with Applications to Image Processing and Pattern RecognitionThis text deals with the subject of fuzzy algorithms and their applications to image processing and pattern recognition. Subjects covered include membership functions; fuzzy clustering; fuzzy rulers and defuzzification; fuzzy classifiers; and combined classifiers.

Contents:

Introduction:
Basic Concepts of Fuzzy Sets
Fuzzy Relations
Fuzzy Models for Image Processing and Pattern Recognition
Membership Functions:
Introduction
Heuristic Selections
Clustering Approaches
Tuning of Membership Functions
Concluding Remarks
Optimal Image Thresholding:
Introduction
Threshold Selection Based on Statistical Decision Theory
Non-fuzzy Thresholding Algorithms
Fuzzy Thresholding Algorithm
Unified Formulation of Three Thresholding Algorithms
Multilevel Thresholding
Applications
Concluding Remarks
Fuzzy Clustering:
Introduction
C-Means Algorithm
Fuzzy C-Means Algorithm
Comparison between Hard and Fuzzy Clustering Algorithms
Cluster Validity
Applications
Concluding Remarks

Line Pattern Matching:
Introduction
Similarity Measures between Line Segments
Basic Matching Algorithm
Dealing with Noisy Patterns
Dealing with Rotated Patterns
Applications
Concluding Remarks
Fuzzy Rule-based Systems:
Introduction
Learning from Examples
Decision Tree Approach
Fuzzy Aggregation Network Approach
Minimization of Fuzzy Rules
Defuzzification and Optimization
Applications
Concluding Remarks
Combined Classifiers:
Introduction
Voting Schemes
Maximum Posteriori Probability
Multilayer Perceptron Approach
Fuzzy Measures and Fuzzy Integrals
Applications
Concluding Remarks

Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems
Fuzzy Logic for Business,Finance, and Management
Fuzzy logic - Wikipedia, the free encyclopedia
Fuzzy Logic Tutorial - An Introduction
Other Fuzzy Logic Books
Download

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Nikola K. Kasabov
A Bradford Book
The MIT Press
Cambridge, Massachusetts
London, England

Contents
Foreword by Shun-ichi Amari xi
Preface xiii
1 The Faculty of Knowledge Engineering and Problem Solving 1
1.1 Introduction to AI paradigms 1
1.2 Heuristic problem solving; genetic algorithms 3
1.3 Why expert systems, fuzzy systems, neural networks, and hybrid systems
for knowledge engineering and problem solving?
14
1.4 Generic and specific AI problems: Pattern recognition and classification 19
1.5 Speech and language processing 28
1.6 Prediction 42
1.7 Planning, monitoring, diagnosis, and control 49
1.8 Optimization, decision making, and games playing 57
1.9 A general approach to knowledge engineering 65
1.10 Problems and exercises 68
1.11 Conclusion 72
1.12 Suggested reading 732 Knowledge Engineering and Symbolic Artificial Intelligence 75
2.1 Data, information, and knowledge: Major issues in knowledge engineering 75
2.2 Data analysis, data representation, and data transformation 80
2.3 Information structures and knowledge representation 89
2.4 Methods for symbol manipulation and inference: Inference as matching;
inference as a search
100
2.5 Propositional logic 110
2.6 Predicate logic: PROLOG 113
2.7 Production systems 118
2.8 Expert systems 128
2.9 Uncertainties in knowledge-based systems: Probabilistic methods 132
2.10 Nonprobabilistic methods for dealing with uncertainties 140
2.11 Machine-learning methods for knowledge engineering 146
2.12 Problems and exercises 155
2.13 Conclusion 164
2.14 Suggested reading 164
3 From Fuzzy Sets to Fuzzy Systems 167
3.1 Fuzzy sets and fuzzy operations 167
3.2 Fuzziness and probability;
conceptualizing in fuzzy terms; the
extension principle
175
3.3 Fuzzy relations and fuzzy
implications; fuzzy propositions and
fuzzy logic
184
3.4 Fuzzy rules, fuzzy inference
methods, fuzzification and
defuzzification
192
3.5 Fuzzy systems as universal
approximators; Interpolation of
fuzzy rules
205
3.6 Fuzzy information retrieval and
fuzzy databases
208
3.7 Fuzzy expert systems 215
3.8 Pattern recognition and
classification, fuzzy clustering,
image and speech processing
223
3.9 Fuzzy systems for prediction 229
3.10 Control, monitoring, diagnosis,
and planning
230
3.11 Optimization and decision
making
234
3.12 Problems and exercises 236
3.13 Conclusion 248
3.14 Suggested reading 249
4 Neural Networks: Theoretical and
Computational Models
251
4.1 Real and artificial neurons 251
4.2 Supervised learning in neural
networks: Perceptrons and
multilayer perceptrons
267
4.3 Radial basis functions, time-
delay neural networks, recurrent
networks
282
4.4 Neural network models for
unsupervised learning:
288 4.5 Kohonen self-organizing
topological maps
293
4.6 Neural networks as associative
memories
300
4.7 On the variety of neural network
models
307
4.8 Fuzzy neurons and fuzzy neural
networks
314
4.9 Hierarchical and modular
connectionist systems
320
4.10 Problems 323
4.11 Conclusion 3284.12 Suggested reading 329

Page ix
5 Neural Networks for Knowledge Engineering and Problem Solving 331
5.1 Neural networks as a problem-solving paradigm 331
5.2 Connectionist expert systems 340
5.3 Connectionist models for knowledge acquisition: One rule is worth a
thousand data examples
347
5.4 Symbolic rules insertion in neural networks: Connectionist production
systems
359
5.5 Connectionist systems for pattern recognition and classification; image
processing
365
5.6 Connectionist systems for speech processing 375
5.7 Connectionist systems for prediction 388
5.8 Connectionist systems for monitoring, control, diagnosis, and planning 398
5.9 Connectionist systems for optimization and decision making 402
5.10 Connectionist systems for modeling strategic games 405
5.11 Problems 409
5.12 Conclusions 418
5.13 Suggested reading 4186 Hybrid Symbolic, Fuzzy, and Connectionist Systems: Toward Comprehensive
Artificial Intelligence
421
6.1 The hybrid systems paradigm 421
6.2 Hybrid connectionist production systems 429
6.3 Hybrid connectionist logic programming systems 433
6.4 Hybrid fuzzy connectionist production systems 435
6.5 ("Pure") connectionist production systems: The NPS architecture
(optional)
442
6.6 Hybrid systems for speech and language processing 455
6.7 Hybrid systems for decision making 460
6.8 Problems 462
6.9 Conclusion 473
6.10 Suggested reading 473
7 Neural Networks, Fuzzy Systems and Nonlinear Dynamical Systems Chaos;
Toward New Connectionist and Fuzzy Logic Models
475
7.1 Chaos 475
7.2 Fuzzy systems and chaos: New developments in fuzzy systems 481
Page x
7.3 Neural networks and chaos: New developments in neural networks 486
7.4 Problems 497
7.5 Conclusion 502
7.6 Suggested reading 503
Appendixes 505
References 523
Glossary 539
Index 547


Neural network - Wikipedia, the free encyclopedia
Neural Networks
Fuzzy logic - Wikipedia, the free encyclopedia
Fuzzy Logic Tutorial - An Introduction

Neural Networks Theory
Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems
Other Neural Network Books
Other Fuzzy Logic Books
Download

First Course on Fuzzy Theory and Applications

Kwang H. Lee
Table of Contents
Chapter 1.
FUZZY SETS ------------------------------------------------------------- 1
1.1 Sets ------------------------------------------------------------------------- 1
1.2 Operation of Sets ------------------------------------------------------------- 3
1.3 Characteristics of Crisp Set ------------------------------------------------- 5
1.4 Definition of Fuzzy Set ------------------------------------------------------ 7
1.5 Expanding Concepts of Fuzzy Set ---------------------------------------- 14
1.6 Standard Operation of Fuzzy Set ----------------------------------------- 21
[SUMMARY] -------------------------------------------------------------------- 22
[EXERCISES] ------------------------------------------------------------------- 24
Chapter 2.
THE OPERATION OF FUZZY SET --------------------------------- 27
2.1 Standard Operations of Fuzzy Set ---------------------------------------- 27
2.2 Fuzzy Complement --------------------------------------------------------- 28
2.3 Fuzzy Union ----------------------------------------------------------------- 32
2.4 Fuzzy Intersection ---------------------------------------------------------- 35
2.5 Other Operations In Fuzzy Set -------------------------------------------- 38
2.6 T-norms and T-conorms --------------------------------------------------- 45
[SUMMARY] ------------------------------------------------------------------ 47
[EXERCISES] ------------------------------------------------------------------- 51
Chapter 3.
FUZZY RELATION AND COMPOSITION ------------------------ 53
3.1 Crisp Relation --------------------------------------------------------------- 53
3.2 Properties of Relation on a Single Set ---------------------------------- 62
3.3 Fuzzy Relation -------------------------------------------------------------- 68
3.4 Extension of Fuzzy Set ----------------------------------------------------- 80
[SUMMARY] -------------------------------------------------------------------- 86
[EXERCISES] ------------------------------------------------------------------- 88
Chapter 4.
FUZZY GRAPH AND RELATION ---------------------------------- 91
4.1 Fuzzy Graph ----------------------------------------------------------------- 91
4.2 Characteristics of Fuzzy Relation --------------------------------------- 103
4.3 Classification of Fuzzy Relation ---------------------------------------- 108
4.4 Other Fuzzy Relations ---------------------------------------------------- 116
[SUMMARY] ------------------------------------------------------------------ 124
[EXERCISES] ------------------------------------------------------------------ 126
Chapter 5.
FUZZY NUMBER ----------------------------------------------------- 129
5.1 Concept of Fuzzy Number ----------------------------------------------- 129
5.2 Operation of Fuzzy Number --------------------------------------------- 132
5.3 Triangular Fuzzy Number ------------------------------------------------ 137
5.4 Other Types of Fuzzy Number ------------------------------------------ 145
[SUMMARY] ------------------------------------------------------------------ 149
[EXERCISES] ------------------------------------------------------------------ 150
Chapter 6.
FUZZY FUNCTION -------------------------------------------------- 153
6.1 Kinds of Fuzzy Function ------------------------------------------------- 153
6.2 Fuzzy Extrema of Function ---------------------------------------------- 158
6.3 Integration and Differenciation of Fuzzy Function ------------------- 163
[SUMMARY] ------------------------------------------------------------------ 168
[EXERCISES] ------------------------------------------------------------------ 169
Chapter 7.
PROBABILITY AND UNCERTAINTY --------------------------- 171
7.1 Probability and Possibility ----------------------------------------------- 171
7.2 Fuzzy Event ---------------------------------------------------------------- 174
7.3 Uncertainty ----------------------------------------------------------------- 179
7.4 Measure of Fuzziness ----------------------------------------------------- 181
[SUMMARY] ------------------------------------------------------------------ 189
[EXERCISES] ------------------------------------------------------------------ 190
Chapter 8.
FUZZY LOGIC -------------------------------------------------------- 193
8.1 Classical Logic ------------------------------------------------------------ 193
8.2 Fuzzy Logic ---------------------------------------------------------------- 201
8.3 Linguistic Variable -------------------------------------------------------- 204
8.4 Fuzzy Truth Qualifier ---------------------------------------------------- 206
8.5 Representation of Fuzzy Rule ------------------------------------------- 210
[SUMMARY] ------------------------------------------------------------------ 213
[EXERCISES] ------------------------------------------------------------------ 215
Chapter 9.
FUZZY INFERENCE ------------------------------------------------- 217
9.1 Composition of Rules ----------------------------------------------------- 217
9.2 Fuzzy Rules and Implication -------------------------------------------- 221
9.3 Inference Mechanism ----------------------------------------------------- 224
9.4 Inference Methods -------------------------------------------------------- 236
[SUMMARY] ------------------------------------------------------------------ 247
[EXERCISES] ------------------------------------------------------------------ 250
Chapter 10.
FUZZY CONTROL AND FUZZY EXPERT SYSTEMS ------- 253
10.1 Fuzzy Logic Controller ------------------------------------------------ 253
10.2 Fuzzification Interface Component ----------------------------------- 255
10.3 Knowledge Base Component ------------------------------------------ 257
10.4 Inference (Decision Making Logic) ----------------------------------- 265
10.5 Defuzzification ----------------------------------------------------------- 269
10.6 Design Procedure of Fuzzy Logic Controller ------------------------ 272
10.7 Application Example of FLC Design --------------------------------- 273
10.8 Fuzzy Expert Systems --------------------------------------------------- 277
[SUMMARY] ------------------------------------------------------------------ 280
[EXERCISES] ------------------------------------------------------------------ 282
Chapter 11.
FUSION OF FUZZY SYSTEM AND NEURAL NETWORKS ------- 285
11.1 Neural Networks --------------------------------------------------------- 285
11.2 Fusion With Neural Networks ----------------------------------------- 290
[SUMMARY] ------------------------------------------------------------------ 306
[EXERCISES] ------------------------------------------------------------------ 308
Chapter 12.
FUSION OF FUZZY SYSTEM AND GENETIC ALGORITHMS ---- 309
12.1 Genetic Algorithms ------------------------------------------------------ 309
12.2 Fusion With Genetic Algorithms -------------------------------------- 314
[SUMMARY] ------------------------------------------------------------------ 323
[EXERCISES] ------------------------------------------------------------------ 324
BIBLIOGRAPH -------------------------------------------------------- 325
INDEX ------------------------------------------------------------------- 333

Fuzzy logic - Wikipedia, the free encyclopedia
Fuzzy Logic Tutorial - An Introduction
Fuzzy Logic and Its Uses
Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems
Fuzzy Logic for Business,Finance, and Management
Other Fuzzy Logic Books
Download

Fuzzy Logic for Business,Finance, and Management

Contents
Foreword xi
Preface to the Second Edition xiii
Preface to the First Edition xv
List of Case Studies xix
1 Fuzzy Sets 1
1.1 Classical Sets: Relations and Functions . . . . . . . . . 1
1.2 Definition of Fuzzy Sets . . . . . . . . . . . . . . . . . . 9
1.3 Basic Operations on Fuzzy Sets . . . . . . . . . . . . . . 15
1.4 Fuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . . 19
1.5 Triangular Fuzzy Numbers . . . . . . . . . . . . . . . . . 22
1.6 Trapezoidal Fuzzy Numbers . . . . . . . . . . . . . . . . 24
1.7 Fuzzy Relations . . . . . . . . . . . . . . . . . . . . . . . 26
1.8 Basic Operations on Fuzzy Relations . . . . . . . . . . . 29
1.9 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 Fuzzy Logic 37
2.1 Basic Concepts of Classical Logic . . . . . . . . . . . . . 37
2.2 Many-Valued Logic . . . . . . . . . . . . . . . . . . . . . 41
2.3 What is Fuzzy Logic? . . . . . . . . . . . . . . . . . . . 43
2.4 Linguistic Variables . . . . . . . . . . . . . . . . . . . . . 44
2.5 Linguistic Modifiers . . . . . . . . . . . . . . . . . . . . 46
2.6 Composition Rules for Fuzzy Propositions . . . . . . . . 50
2.7 Semantic Entailment . . . . . . . . . . . . . . . . . . . . 54
2.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3 Fuzzy Averaging for Forecasting 61
3.1 Statistical Average . . . . . . . . . . . . . . . . . . . . . 61
3.2 Arithmetic Operations with Triangular and Trapezoidal
Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3 Fuzzy Averaging . . . . . . . . . . . . . . . . . . . . . . 66
3.4 Fuzzy Delphi Method for Forecasting . . . . . . . . . . . 71
3.5 Weighted Fuzzy Delphi Method . . . . . . . . . . . . . . 76
3.6 Fuzzy PERT for Project Management . . . . . . . . . . 77
3.7 Forecasting Demand . . . . . . . . . . . . . . . . . . . . 87
3.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4 Decision Making in a Fuzzy Environment 91
4.1 Decision Making by Intersection of Fuzzy Goals and Con-
straints . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2 Various Applications . . . . . . . . . . . . . . . . . . . . 95
4.3 Pricing Models for New Products . . . . . . . . . . . . . 104
4.4 Fuzzy Averaging for Decision Making . . . . . . . . . . 110
4.5 Multi-Expert Decision Making . . . . . . . . . . . . . . 115
4.6 Fuzzy Zero-Based Budgeting . . . . . . . . . . . . . . . 119
4.7 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5 Fuzzy Logic Control for Business, Finance, and Manage-
ment 127
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.2 Modeling the Control Variables . . . . . . . . . . . . . . 129
5.3 If . . . and . . . Then Rules . . . . . . . . . . . . . . . . . . 133
5.4 Rule Evaluation . . . . . . . . . . . . . . . . . . . . . . . 136
5.5 Aggregation (Conflict Resolution) . . . . . . . . . . . . . 138
5.6 Defuzzification . . . . . . . . . . . . . . . . . . . . . . . 144
5.7 Use of Singletons to Model Outputs . . . . . . . . . . . 149
5.8 Tuning of Fuzzy Logic Control Models . . . . . . . . . . 150
5.9 One-Input–One-Output Control Model . . . . . . . . . . 152
5.10 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6 Applications of Fuzzy Logic Control 157
6.1 Investment Advisory Models . . . . . . . . . . . . . . . . 157
6.2 Fuzzy Logic Control for Pest Management . . . . . . . . 164
6.3 Inventory Control Models . . . . . . . . . . . . . . . . . 170
6.4 Problem Analysis . . . . . . . . . . . . . . . . . . . . . . 177
6.5 Potential Problem Analysis . . . . . . . . . . . . . . . . 182
6.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7 Fuzzy Queries from Databases: Applications 187
7.1 Standard Relational Databases . . . . . . . . . . . . . . 187
7.2 Fuzzy Queries . . . . . . . . . . . . . . . . . . . . . . . . 190
7.3 Fuzzy Complex Queries . . . . . . . . . . . . . . . . . . 196
7.4 Fuzzy Queries for Small Manufacturing Companies . . . 199
7.5 Fuzzy Queries for Stocks and Funds Databases . . . . . 206
7.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
References 217
Index 223


Another Fuzzy Logic Books
Download

Cost Optimization of Structures

Contents
Preface xi
Acknowledgments xiii
About the Authors xv
1 Introduction 1
1.1 The Case for Cost Optimization 1
1.2 Cost Optimization of Concrete Structures 2
1.2.1 Concrete Beams and Slabs 3
1.2.2 Concrete Columns 11
1.2.3 Concrete Frame Structures 12
1.2.4 Bridge Structures 14
1.2.5 Water Tanks 16
1.2.6 Folded Plates and Shear Walls 17
1.2.7 Concrete Pipes 17
1.2.8 Concrete Tensile Members 17
1.2.9 Cost Optimization Using the Reliability Theory 18
1.2.10 Concluding Comments 20
1.3 Cost Optimization of Steel Structures 20
1.3.1 Deterministic Cost Optimization 20
1.3.2 Cost Optimization Using the Reliability Theory 31
1.3.3 Fuzzy Optimization 33
1.3.4 Concluding Comments 35
2 Evolutionary Computing and the Genetic Algorithm 37
2.1 Overview and Basic Operations 37
2.2 Coding and Decoding 39viii Contents
2.3 Basic Operations in Genetic Algorithms 40
2.4 GA with the Penalty Function Method 43
2.4.1 Problem Formulation for Axial Force (Truss)
Structures 43
2.4.2 Genetic Algorithm with the Penalty Function
Method 45
2.5 Augmented Lagrangian Method 47
2.6 GA with the Augmented Lagrangian Method 48
2.6.1 Problem Formulation for Axial Force (Truss)
Structures 48
2.6.2 Genetic Algorithm with the Augmented Lagrangian
Method 49
3 Cost Optimization of Composite Floors 53
3.1 Introduction 53
3.2 Minimum Cost Design of Composite Beams 54
3.2.1 Cost Function 54
3.2.2 Constraints 55
3.2.3 Problem Formulation as a Mixed Integer–Discrete
Nonlinear Programming Problem 61
3.3 Solution by the Floating-Point Genetic Algorithm 62
3.3.1 Binary Versus Floating-Point GA 62
3.3.2 Crossover Operation for the Floating-Point GA 62
3.3.3 Mutation Operation for the Floating-Point GA 63
3.3.4 Floating-Point GA for Cost Optimization
of Composite Floors 63
3.4 Solution by the Neural Dynamics Method 65
3.5 Counter Propagation Neural (CPN) Network for Function
Approximations 68
3.6 Examples 71
3.6.1 Example 1 71
3.6.2 Example 2 72
4 Fuzzy Genetic Algorithm for Optimization of Steel Structures 77
4.1 Introduction 77
4.2 Fuzzy Set Theory and Structural Optimization 79
4.3 Minimum Weight Design of Axially Loaded Space
Structures 82
4.4 Fuzzy Membership Functions 85
4.5 Fuzzy Augmented Lagrangian Genetic Algorithm 87Contents ix
4.6 Implementation and Examples 92
4.6.1 Example 1 93
4.6.2 Example 2 93
4.7 Conclusion 98
5 Fuzzy Discrete Multi-criteria Cost Optimization of Steel
Structures 101
5.1 Cost of a Steel Structure 101
5.2 Primary Contributing Factors to the Cost of a Steel
Structure 102
5.3 Fuzzy Discrete Multi-criteria Cost Optimization 105
5.4 Membership Functions 110
5.4.1 Membership Function for Minimum Cost 110
5.4.2 Membership Function for Minimum Weight 110
5.4.3 Membership Function for Minimum Number of
Section Types 110
5.5 Fuzzy Membership Functions for Criteria with Unequal
Imortance 112
5.6 Pareto Optimality 112
5.7 Selection of Commercially Available Discrete Shapes 114
5.8 Implementation and a Parametric Study 117
5.9 Application to High-Rise Steel Structures 118
5.9.1 Example 1 118
5.9.2 Example 2 119
5.10 Concluding Comments 123
6 Parallel Computing 125
6.1 Multiprocessor Computing Environment 125
6.2 Parallel Processing Implementation Environment 128
6.2.1 OpenMP Data Parallel Application Programming
Interface (API) 128
6.2.2 Message Passing Interface (MPI) 130
6.3 Performance Optimization of Parallel Programs 130
7 Parallel Fuzzy Genetic Algorithms for Cost Optimization of
Large Steel Structures 133
7.1 Genetic Algorithm and Parallel Processing 133
7.2 Cost Optimization of Moment-Resisting Steel Space
Structures 135
7.3 Data Parallel Fuzzy Genetic Algorithm for Optimization
of Steel Structures Using OpenMP 136
7.4 Distributed Parallel Fuzzy Genetic Algorithm for
Optimization of Steel Structures Using MPI 138
7.4.1 Processor Farming Scheme 138
7.4.2 Migration Scheme 140
7.5 Bilevel Parallel Fuzzy GA for Optimization of Steel
Structures Using OpenMP and MPI 142
7.5.1 Bilevel Parallel Fuzzy GA with the Processor
Farming Scheme 145
7.5.2 Bilevel Parallel Fuzzy GA with the Migration Scheme 146
7.6 Application to High-Rise Building Steel Structures 147
7.6.1 Example 1 147
7.6.2 Example 2 149
7.7 Parallel Processing Performance Evaluation 155
7.7.1 Data Parallel Fuzzy GA Using OpenMP 155
7.7.2 Distributed Parallel Fuzzy GA Using MPI 157
7.7.3 Bilevel Parallel Fuzzy GA Using OpenMP and MPI 160
7.8 Concluding Comments 164
8 Life-Cycle Cost Optimization of Steel Structures 165
8.1 Introduction 165
8.2 Life-Cycle Cost of a Steel Structure and the Primary
Contributing Factors 167
8.3 Formulation of the Total Life-Cycle Cost 170
8.4 Fuzzy Discrete Multi-criteria Life-Cycle Cost Optimization 171
8.5 Application to a High-Rise Building Steel Structure 174
Appendix A 177
Appendix B 181
References 185
Index 201


Another Fuzzy Logic Books
Another Genetic Algorithm Books
Another Parallel Programming Books
Download

Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications

Preface
Chapter 1—Introduction to Neural Networks, Fuzzy Systems, Genetic
Algorithms, and their Fusion
1. Knowledge-Based Information Systems
2. Artificial Neural Networks
3. Evolutionary Computing
4. Fuzzy Logic
5. Fusion
6. Summary
References
Chapter 2—A New Fuzzy-Neural Controller
1. Introduction
2. RBF Based Fuzzy System with Unsupervised Learning
2.1 Fuzzy System Based on RBF
2.2 Coding
2.3 Selection
2.4 Crossover Operator
2.5 Mutation Operator
3. Hierarchical Fuzzy-Neuro Controller Based on Skill Knowledge Database
4. Fuzzy-Neuro Controller for Cart-Pole System
5. Conclusions
References
Chapter 3—Expert Knowledge-Based Direct Frequency Converter Using
Fuzzy Logic Control
1. Introduction
2. XDFC Topology and Operation
3. Space Vector Model of the DFC
4. Expert Knowledge-Based SVM
5. XDFC Control
5.1 XDFC Control Strategy and Operation
5.2 Fuzzy Logic Controller
5.3 Load’s Line Current Control
5.4 Input’s Line Current Control
6. Results
7. Evaluation
8. Conclusion
References
Chapter 4—Design of an Electro-Hydraulic System Using Neuro-Fuzzy
Techniques
1. Introduction
2. The Fuzzy Logic System
2.1 Fuzzification
2.2 Inference Mechanism
2.3 Defuzzification
3. Fuzzy Modeling
4. The Learning Mechanism
4.1 Model Initialization
4.2 The Cluster-Based Algorithm
4.3 Illustrative Example
4.4 The Neuro-Fuzzy Algorithm
5. The Experimental System
5.1 Training Data Generation
6. Neuro-Fuzzy Modeling of the Electro-Hydraulic Actuator
7. The Neuro-Fuzzy Control System
7.1 Experimental Results
8. Conclusion
References
Chapter 5—Neural Fuzzy Based Intelligent Systems and Applications
1. Introduction
2. Advantages and Disadvantages of Fuzzy Logic and Neural Nets
2.1 Advantages of Fuzzy Logic
2.2 Disadvantages of Fuzzy Logic
2.3 Advantages of Neural Nets2.4 Disadvantages of Neural Nets
3. Capabilities of Neural Fuzzy Systems (NFS)
4. Types of Neural Fuzzy Systems
5. Descriptions of a Few Neural Fuzzy Systems
5.1 NeuFuz
5.1.1 Brief Overview
5.1.2 NeuFuz Architecture
5.1.3 Fuzzy Logic Processing
5.2 Recurrent Neural Fuzzy System (RNFS)
5.2.1 Recurrent Neural Net
5.2.2 Temporal Information and Weight Update
5.2.3 Recurrent Fuzzy Logic
5.2.4 Determining the Number of Time Delays
6. Representative Applications
6.1 Motor Control
6.1.1 Choosing the Inputs and Outputs
6.1.2 Data Collection and Training
6.1.3 Rule Evaluation and Optimization
6.1.4 Results and Comparison with the PID Approach
6.2 Toaster Control
6.3 Speech Recognition using RNFS
6.3.1 Small Vocabulary Word Recognition
6.3.2 Training and Testing
7. Conclusion
References
Chapter 6—Vehicle Routing through Simulation of Natural Processes
1. Introduction
2. Vehicle Routing Problems
3. Neural Networks
3.1 Self-Organizing Maps
3.1.1 Vehicle Routing Applications
3.1.2 The Hierarchical Deformable Net
3.2 Feedforward Models
3.2.1 Dynamic vehicle routing and dispatching
3.2.2 Feedforward Neural Network Model with Backpropagation
3.2.3 An Application for a Courier Service
4. Genetic Algorithms
4.1 Genetic clustering
4.1.1 Genetic Sectoring (GenSect)
4.1.2 Genetic Clustering with Geometric Shapes (GenClust)
4.1.3 Real-World Applications
4.2 Decoders4.3 A Nonstandard GA
5. Conclusion
Acknowledgments
References
Chapter 7—Fuzzy Logic and Neural Networks in Fault Detection
1. Introduction
2. Fault Diagnosis
2.1 Concept of Fault Diagnosis
2.2 Different Approaches for Residual Generation and Residual Evaluation
3. Fuzzy Logic in Fault Detection
3.1 A Fuzzy Filter for Residual Evaluation
3.1.1 Structure of the Fuzzy Filter
3.1.2 Supporting Algorithm for the Design of the Fuzzy Filter
3.2 Application of the Fuzzy Filter to a Wastewater Plant
3.2.1 Description of the Process
3.2.2 Design of the Fuzzy Filter for Residual Evaluation
3.2.3 Simulation Results
4. Neural Networks in Fault Detection
4.1 Neural Networks for Residual Generation
4.1.1 Radial-Basis-Function(RBF) Neural Networks
4.1.2 Recurrent Neural Networks (RNN)
4.2 Neural Networks for Residual Evaluation
4.2.1 Restricted-Coulomb-Energy (RCE) Neural Networks
4.3 Application to the Industrial Actuator Benchmark Test
4.3.1 Simulation Results for Residual Generation
4.3.2 Simulation Results for Residual Evaluation
5. Conclusions
References
Chapter 8—Application of the Neural Network and Fuzzy Logic to the
Rotating Machine Diagnosis
1. Introduction
2. Rotating Machine Diagnosis
2.1 Fault Diagnosis Technique for Rotating Machines
3. Application of Neural Networks and Fuzzy Logic for Rotating Machine Diagnosis
3.1 Fault Diagnosis Using a Neural Network
3.2 Fault Diagnosis Using Fuzzy Logic
4. Conclusion
References
Chapter 9—Fuzzy Expert Systems in ATM Networks
1. Introduction

Another Neural Network Books
Another Fuzzy Logic Books
Another Genetic Algorithm Books
Download

Advances in Fuzzy Clustering and its Applications

Contents
List of Contributors xi
Foreword xv
Preface xvii
Part I Fundamentals 1
1 Fundamentals of Fuzzy Clustering 3
Rudolf Kruse, Christian Do¨ring and Marie-Jeanne Lesot
1.1 Introduction 3
1.2 Basic Clustering Algorithms 4
1.3 Distance Function Variants 14
1.4 Objective Function Variants 18
1.5 Update Equation Variants: Alternating Cluster Estimation 25
1.6 Concluding Remarks 27
Acknowledgements 28
References 29
2 Relational Fuzzy Clustering 31
Thomas A. Runkler
2.1 Introduction 31
2.2 Object and Relational Data 31
2.3 Object Data Clustering Models 34
2.4 Relational Clustering 38
2.5 Relational Clustering with Non-spherical Prototypes 41
2.6 Relational Data Interpreted as Object Data 45
2.7 Summary 46
2.8 Experiments 46
2.9 Conclusions 49
References 50
3 Fuzzy Clustering with Minkowski Distance Functions 53
Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen
3.1 Introduction 53
3.2 Formalization 54
3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances 56
3.4 The Effects of the Robustness Parameter l 60
3.5 Internet Attitudes 62
3.6 Conclusions 65
References 66
4 Soft Cluster Ensembles 69
Kunal Punera and Joydeep Ghosh
4.1 Introduction 69
4.2 Cluster Ensembles 71
4.3 Soft Cluster Ensembles 75
4.4 Experimental Setup 78
4.5 Soft vs. Hard Cluster Ensembles 82
4.6 Conclusions and Future Work 90
Acknowledgements 90
References 90
Part II Visualization 93
5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy
Similarity Measures 95
Ja´nos Abonyi and Bala´zs Feil
5.1 Problem Definition 97
5.2 Classical Methods for Cluster Validity and Merging 99
5.3 Similarity of Fuzzy Clusters 100
5.4 Visualization of Clustering Results 103
5.5 Conclusions 116
Appendix 5A.1 Validity Indices 117
Appendix 5A.2 The Modified Sammon Mapping Algorithm 120
Acknowledgements 120
References 120
6 Interactive Exploration of Fuzzy Clusters 123
Bernd Wiswedel, David E. Patterson and Michael R. Berthold
6.1 Introduction 123
6.2 Neighborgram Clustering 125
6.3 Interactive Exploration 131
6.4 Parallel Universes 135
6.5 Discussion 136
References 136
Part III Algorithms and Computational Aspects 137
7 Fuzzy Clustering with Participatory Learning and Applications 139
Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager
7.1 Introduction 139
7.2 Participatory Learning 140
7.3 Participatory Learning in Fuzzy Clustering 142
7.4 Experimental Results 145
7.5 Applications 148
7.6 Conclusions 152
Acknowledgements 152
References 152
8 Fuzzy Clustering of Fuzzy Data 155
Pierpaolo D’Urso
8.1 Introduction 155
8.2 Informational Paradigm, Fuzziness and Complexity
in Clustering Processes 156
8.3 Fuzzy Data 160
8.4 Fuzzy Clustering of Fuzzy Data 165
8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays 176
8.6 Applicative Examples 180
8.7 Concluding Remarks and Future Perspectives 187
References 189
9 Inclusion-based Fuzzy Clustering 193
Samia Nefti-Meziani and Mourad Oussalah
9.1 Introduction 193
9.2 Background: Fuzzy Clustering 195
9.3 Construction of an Inclusion Index 196
9.4 Inclusion-based Fuzzy Clustering 198
9.5 Numerical Examples and Illustrations 201
9.6 Conclusions 206
Acknowledgements 206
Appendix 9A.1 207
References 208
10 Mining Diagnostic Rules Using Fuzzy Clustering 211
Giovanna Castellano, Anna M. Fanelli and Corrado Mencar
10.1 Introduction 211
10.2 Fuzzy Medical Diagnosis 212
10.3 Interpretability in Fuzzy Medical Diagnosis 213
10.4 A Framework for Mining Interpretable Diagnostic Rules 216
10.5 An Illustrative Example 221
10.6 Concluding Remarks 226
References 226
11 Fuzzy Regression Clustering 229
Mikal Sato-Ilic
11.1 Introduction 229
11.2 Statistical Weighted Regression Models 230
11.3 Fuzzy Regression Clustering Models 232
11.4 Analyses of Residuals on Fuzzy Regression Clustering Models 237
11.5 Numerical Examples 242
11.6 Conclusion 245
References 245
12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling
Using the Weighted Fuzzy C-means 247
George E. Tsekouras
12.1 Introduction 247
12.2 Takagi and Sugeno’s Fuzzy Model 248
12.3 Hierarchical Clustering-based Fuzzy Modeling 249
12.4 Simulation Studies 256
12.5 Conclusions 261
References 261
13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data 265
Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni
13.1 Introduction 265
13.2 Dissimilarity Modeling 267
13.3 Relational Clustering 275
13.4 Experimental Results 280
13.5 Conclusions 281
References 281
14 Simultaneous Clustering and Feature Discrimination
with Applications 285
Hichem Frigui
14.1 Introduction 285
14.2 Background 287
14.3 Simultaneous Clustering and Attribute Discrimination (SCAD) 289
14.4 Clustering and Subset Feature Weighting 296
14.5 Case of Unknown Number of Clusters 298
14.6 Application 1: Color Image Segmentation 298
14.7 Application 2: Text Document Categorization and Annotation 302
14.8 Application 3: Building a Multi-modal Thesaurus from Annotated Images 305
14.9 Conclusions 309
Appendix 14A.1 310
Acknowledgements 311
References 311
Part IV Real-time and Dynamic Clustering 313
15 Fuzzy Clustering in Dynamic Data Mining – Techniques and Applications 315
Richard Weber
15.1 Introduction 315
15.2 Review of Literature Related to Dynamic Clustering 315
15.3 Recent Approaches for Dynamic Fuzzy Clustering 317
15.4 Applications 324
15.5 Future Perspectives and Conclusions 331
Acknowledgement 331
References 331
16 Fuzzy Clustering of Parallel Data Streams 333
Ju¨rgen Beringer and Eyke Hu¨llermeier
16.1 Introduction 333
16.2 Background 334
16.3 Preprocessing and Maintaining Data Streams 336
16.4 Fuzzy Clustering of Data Streams 340
16.5 Quality Measures 343
16.6 Experimental Validation 345
16.7 Conclusions 350
References 351
17 Algorithms for Real-time Clustering and Generation of Rules from Data 353
Dimitar Filev and Plamer Angelov
17.1 Introduction 353
17.2 Density-based Real-time Clustering 355
17.3 FSPC: Real-time Learning of Simplified Mamdani Models 358
17.4 Applications 362
17.5 Conclusion 367
References 368
Part V Applications and Case Studies 371
18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM
with Feature Partitions 373
Mark D. Alexiuk and Nick J. Pizzi
18.1 Introduction 373
18.2 FCM with Feature Partitions 374
18.3 Magnetic Resonance Imaging 379
18.4 FMRI Analysis with FCMP 381
18.5 Data-sets 382
18.6 Results and Discussion 384
18.7 Conclusion 390
Acknowledgements 390
References 390
19 Concept Induction via Fuzzy C-means Clustering in a
High-dimensional Semantic Space 393
Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau
19.1 Introduction 393
19.2 Constructing a High-dimensional Semantic Space via Hyperspace
Analogue to Language 395
19.3 Fuzzy C-means Clustering 397
19.4 Word Clustering on a HAL Space – A Case Study 399
19.5 Conclusions and Future Work 402
Acknowledgement 402
References 402
20 Novel Developments in Fuzzy Clustering for the Classification
of Cancerous Cells using FTIR Spectroscopy 405
Xiao-Ying Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George
20.1 Introduction 405
20.2 Clustering Techniques 406
20.3 Cluster Validity 412
20.4 Simulated Annealing Fuzzy Clustering Algorithm 413
20.5 Automatic Cluster Merging Method 418
20.6 Conclusion 423
Acknowledgements 424
References 424
Index 427

Another Fuzzy Logic
Another Parallel Programming
Download

Fuzzy Control

Contents
PREFACE vii
CHAPTER 1 / Introduction 1
1.1 Overview 1
1.2 Conventional Control System Design 3
1.2.1 Mathematical Modeling 3
1.2.2 Performance Objectives and Design Constraints 5
1.2.3 Controller Design 7
1.2.4 Performance Evaluation 8
1.3 Fuzzy Control System Design 10
1.3.1 Modeling Issues and Performance Objectives 12
1.3.2 Fuzzy Controller Design 12
1.3.3 Performance Evaluation 13
1.3.4 Application Areas 14
1.4 What This Book Is About 14
1.4.1 What the Techniques Are Good For: An Example 15
1.4.2 Objectives of This Book 17
1.5 Summary 18
1.6 For Further Study 19
1.7 Exercises 19
CHAPTER 2 / Fuzzy Control: The Basics 23
2.1 Overview 23
2.2 Fuzzy Control: A Tutorial Introduction 24
2.2.1 Choosing Fuzzy Controller Inputs and Outputs 26
2.2.2 Putting Control Knowledge into Rule-Bases 27
2.2.3 Fuzzy Quantification of Knowledge 32
2.2.4 Matching: Determining Which Rules to Use 37
2.2.5 Inference Step: Determining Conclusions 42
2.2.6 Converting Decisions into Actions 44
2.2.7 Graphical Depiction of Fuzzy Decision Making 49
2.2.8 Visualizing the Fuzzy Controller’s Dynamical Operation 50
2.3 General Fuzzy Systems 51
2.3.1 Linguistic Variables, Values, and Rules 52
2.3.2 Fuzzy Sets, Fuzzy Logic, and the Rule-Base 55
2.3.3 Fuzzification 61
2.3.4 The Inference Mechanism 62
2.3.5 Defuzzification 65
2.3.6 Mathematical Representations of Fuzzy Systems 69
2.3.7 Takagi-Sugeno Fuzzy Systems 73
2.3.8 Fuzzy Systems Are Universal Approximators 77
2.4 Simple Design Example: The Inverted Pendulum 77
2.4.1 Tuning via Scaling Universes of Discourse 78
2.4.2 Tuning Membership Functions 83
2.4.3 The Nonlinear Surface for the Fuzzy Controller 87
2.4.4 Summary: Basic Design Guidelines 89
2.5 Simulation of Fuzzy Control Systems 91
2.5.1 Simulation of Nonlinear Systems 91
2.5.2 Fuzzy Controller Arrays and Subroutines 94
2.5.3 Fuzzy Controller Pseudocode 95
2.6 Real-Time Implementation Issues 97
2.6.1 Computation Time 97
2.6.2 Memory Requirements 98
2.7 Summary 99
2.8 For Further Study 101
2.9 Exercises 101
2.10 Design Problems 110
CHAPTER 3 / Case Studies in Design and Implementation 119
3.1 Overview 119
3.2 Design Methodology 122
3.3 Vibration Damping for a Flexible Robot 124
3.3.1 The Two-Link Flexible Robot 125
3.3.2 Uncoupled Direct Fuzzy Control 129
3.3.3 Coupled Direct Fuzzy Control 134
3.4 Balancing a Rotational Inverted Pendulum 142
3.4.1 The Rotational Inverted Pendulum 142
3.4.2 A Conventional Approach to Balancing Control 144
3.4.3 Fuzzy Control for Balancing 145
3.5 Machine Scheduling 152
3.5.1 Conventional Scheduling Policies 153
3.5.2 Fuzzy Scheduler for a Single Machine 156
3.5.3 Fuzzy Versus Conventional Schedulers 158
3.6 Fuzzy Decision-Making Systems 161
3.6.1 Infectious Disease Warning System 162
3.6.2 Failure Warning System for an Aircraft 166
3.7 Summary 168
3.8 For Further Study 169
3.9 Exercises 170
3.10 Design Problems 172
CHAPTER 4 / Nonlinear Analysis 187
4.1 Overview 187
4.2 Parameterized Fuzzy Controllers 189
4.2.1 Proportional Fuzzy Controller 190
4.2.2 Proportional-Derivative Fuzzy Controller 191
4.3 Lyapunov Stability Analysis 193
4.3.1 Mathematical Preliminaries 193
4.3.2 Lyapunov’s Direct Method 195
4.3.3 Lyapunov’s Indirect Method 196
4.3.4 Example: Inverted Pendulum 197
4.3.5 Example: The Parallel Distributed Compensator 200
4.4 Absolute Stability and the Circle Criterion 204
4.4.1 Analysis of Absolute Stability 204
4.4.2 Example: Temperature Control 208
4.5 Analysis of Steady-State Tracking Error 210
4.5.1 Theory of Tracking Error for Nonlinear Systems 211
4.5.2 Example: Hydrofoil Controller Design 213
4.6 Describing Function Analysis 214
4.6.1 Predicting the Existence and Stability of Limit Cycles 214
4.6.2 SISO Example: Underwater Vehicle Control System 218
4.6.3 MISO Example: Tape Drive Servo 219
4.7 Limitations of the Theory 220
4.8 Summary 222
4.9 For Further Study 223
4.10 Exercises 225
4.11 Design Problems 228
CHAPTER 5 / Fuzzy Identification and Estimation 233
5.1 Overview 233
5.2 Fitting Functions to Data 235
5.2.1 The Function Approximation Problem 235
5.2.2 Relation to Identification, Estimation, and Prediction 238
5.2.3 Choosing the Data Set 240
5.2.4 Incorporating Linguistic Information 241
5.2.5 Case Study: Engine Failure Data Sets 243
5.3 Least Squares Methods 248
5.3.1 Batch Least Squares 248
5.3.2 Recursive Least Squares 252
5.3.3 Tuning Fuzzy Systems 255
5.3.4 Example: Batch Least Squares Training of Fuzzy Systems 257
5.3.5 Example: Recursive Least Squares Training of Fuzzy Systems 259
5.4 Gradient Methods 260
5.4.1 Training Standard Fuzzy Systems 260
5.4.2 Implementation Issues and Example 264
5.4.3 Training Takagi-Sugeno Fuzzy Systems 266
5.4.4 Momentum Term and Step Size 269
5.4.5 Newton and Gauss-Newton Methods 270
5.5 Clustering Methods 273
5.5.1 Clustering with Optimal Output Predefuzzification 274
5.5.2 Nearest Neighborhood Clustering 279
5.6 Extracting Rules from Data 282
5.6.1 Learning from Examples (LFE) 282
5.6.2 Modified Learning from Examples (MLFE) 285
5.7 Hybrid Methods 291
5.8 Case Study: FDI for an Engine 292
5.8.1 Experimental Engine and Testing Conditions 293
5.8.2 Fuzzy Estimator Construction and Results 294
5.8.3 Failure Detection and Identification (FDI) Strategy 297
5.9 Summary 301
5.10 For Further Study 302
5.11 Exercises 303
5.12 Design Problems 311
CHAPTER 6 / Adaptive Fuzzy Control 317
6.1 Overview 317
6.2 Fuzzy Model Reference Learning Control (FMRLC) 319
6.2.1 The Fuzzy Controller 320
6.2.2 The Reference Model 324
6.2.3 The Learning Mechanism 325
6.2.4 Alternative Knowledge-Base Modifiers 329
6.2.5 Design Guidelines for the Fuzzy Inverse Model 330
6.3 FMRLC: Design and Implementation Case Studies 333
6.3.1 Cargo Ship Steering 333
6.3.2 Fault-Tolerant Aircraft Control 347
6.3.3 Vibration Damping for a Flexible Robot 357
6.4 Dynamically Focused Learning (DFL) 364
6.4.1 Magnetic Ball Suspension System: Motivation for DFL 365
6.4.2 Auto-Tuning Mechanism 377
6.4.3 Auto-Attentive Mechanism 379
6.4.4 Auto-Attentive Mechanism with Memory 384
6.5 DFL: Design and Implementation Case Studies 388
6.5.1 Rotational Inverted Pendulum 388
6.5.2 Adaptive Machine Scheduling 390
6.6 Indirect Adaptive Fuzzy Control 394
6.6.1 On-Line Identification Methods 394
6.6.2 Adaptive Control for Feedback Linearizable Systems 395
6.6.3 Adaptive Parallel Distributed Compensation 397
6.6.4 Example: Level Control in a Surge Tank 398
6.7 Summary 402
6.8 For Further Study 405
6.9 Exercises 406
6.10 Design Problems 407
CHAPTER 7 / Fuzzy Supervisory Control 413
7.1 Overview 413
7.2 Supervision of Conventional Controllers 415
7.2.1 Fuzzy Tuning of PID Controllers 415
7.2.2 Fuzzy Gain Scheduling 417
7.2.3 Fuzzy Supervision of Conventional Controllers 421
7.3 Supervision of Fuzzy Controllers 422
7.3.1 Rule-Base Supervision 422
7.3.2 Case Study: Vibration Damping for a Flexible Robot 423
7.3.3 Supervised Fuzzy Learning Control 427
7.3.4 Case Study: Fault-Tolerant Aircraft Control 429
7.4 Summary 435
7.5 For Further Study 436
7.6 Design Problems 437
CHAPTER 8 / Perspectives on Fuzzy Control 439
8.1 Overview 439
8.2 Fuzzy Versus Conventional Control 440
8.2.1 Modeling Issues and Design Methodology 440
8.2.2 Stability and Performance Analysis 442
8.2.3 Implementation and General Issues 443
8.3 Neural Networks 444
8.3.1 Multilayer Perceptrons 444
8.3.2 Radial Basis Function Neural Networks 447
8.3.3 Relationships Between Fuzzy Systems and Neural Networks 449
8.4 Genetic Algorithms 451
8.4.1 Genetic Algorithms: A Tutorial 451
8.4.2 Genetic Algorithms for Fuzzy System Design and Tuning 458
8.5 Knowledge-Based Systems 461
8.5.1 Expert Control 461
8.5.2 Planning Systems for Control 462
8.6 Intelligent and Autonomous Control 463
8.6.1 What Is “Intelligent Control”? 464
8.6.2 Architecture and Characteristics 465
8.6.3 Autonomy 467
8.6.4 Example: Intelligent Vehicle and Highway Systems 468
8.7 Summary 471
8.8 For Further Study 472
8.9 Exercises 472
BIBLIOGRAPHY 477
INDEX 495

Another Fuzzy Logic Books
Download

Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Table of Contents
1. Fuzzy Set Theory................................................................................ 1
I. Classical Set Theory.................................................................... 1
A. Fundamental Concepts........................................................... 1
B. Elementary Measure Theory of Sets...................................... 3
II. Fuzzy Set Theory ........................................................................ 5
III. Interval Arithmetic ...................................................................... 9
A. Fundamental Concepts........................................................... 9
B. Interval Arithmetic................................................................. 12
C. Algebraic Properties of Interval Arithmetic........................... 13
D. Measure Theory of Intervals .................................................. 17
E. Properties of the Width of an Interval.................................... 20
F. Interval Evaluation................................................................. 22
G. Interval Matrix Operations..................................................... 25
H. Interval Matrix Equations and Interval Matrix Inversion ...... 30
IV. Operations on Fuzzy Sets ............................................................ 37
A. Fuzzy Subsets ........................................................................ 37
B. Fuzzy Numbers and Their Arithmetic.................................... 42
Problems............................................................................................. 54
References .......................................................................................... 56
2. Fuzzy Logic Theory ........................................................................... 57
I. Classical Logic Theory................................................................ 58
A. Fundamental Concepts........................................................... 58
B. Logical Functions of the Two-Valued Logic ......................... 61
II. The Boolean Algebra................................................................... 62
A. Basic Operations of the Boolean Algebra.............................. 62
B. Basic Properties of the Boolean Algebra ............................... 63
III. Multi-Valued Logic..................................................................... 65
A. Three-Valued Logic............................................................... 65
B. n-Valued Logic ...................................................................... 65
IV. Fuzzy Logic and Approximate Reasoning .................................. 66
V. Fuzzy Relations ........................................................................... 69
VI. Fuzzy Logic Rule Base................................................................ 75
A. Fuzzy IF-THEN Rules ........................................................... 75
B. Fuzzy Logic Rule Base .......................................................... 77
C. Interpretation of Fuzzy IF-THEN Rules ................................ 80
D. Evaluation of Fuzzy IF-THEN Rules..................................... 82
Problems............................................................................................. 84
References .......................................................................................... 87
3. Fuzzy System Modeling ..................................................................... 89
I. Modeling of Static Fuzzy Systems .............................................. 90
A. Fuzzy Logic Description of Input-Output Relations.............. 90
B. Parameters Identification in Static Fuzzy Modeling .............. 96
II. Discrete-Time Dynamic Fuzzy Systems and Their Stability
Analysis....................................................................................... 102
A. Dynamic Fuzzy Systems without Control.............................. 103
B. Dynamic Fuzzy Systems with Control................................... 109
III. Modeling of Continuous-Time Dynamic Fuzzy Control
Systems ....................................................................................... 114
A. Fuzzy Interval Partitioning .................................................... 115
B. Dynamic Fuzzy System Modeling......................................... 119
IV. Stability Analysis of Continuous-Time Dynamic Fuzzy
Systems ....................................................................................... 124
V. Controllability Analysis of Continuous-Time Dynamic
Fuzzy Systems............................................................................. 129
VI. Analysis of Nonlinear Continuous-Time Dynamic Fuzzy
Systems ....................................................................................... 133
Problems............................................................................................. 136
References .......................................................................................... 138
4. Fuzzy Control Systems....................................................................... 139
I. Classical Programmable Logic Control....................................... 140
II. Fuzzy Logic Control (I): A General Model-Free Approach........ 145
A. A Closed-Loop Set-Point Tracking System........................... 147
B. Design Principle of Fuzzy Logic Controllers......................... 150
C. Examples of Model-Free Fuzzy Controller Design ............... 160
III. Fuzzy Logic Control (II): A General Model-Based Approach.... 170
Problems............................................................................................. 178
References .......................................................................................... 182
5. Fuzzy PID Controllers........................................................................ 183
I. Conventional PID Controllers: Design........................................ 183
II. Fuzzy PID Controllers Design .................................................... 192
A. Fuzzy PD Controller .............................................................. 193
B. Fuzzy PI Controller................................................................ 207
C. Fuzzy PI+D Controller........................................................... 209
III. Fuzzy PID Controllers: Stability Analysis .................................. 223
A. BIBO Stability and the Small Gain Theorem ........................ 223
B. BIBO Stability of Fuzzy PD Control Systems....................... 226
C. BIBO Stability of Fuzzy PI Control Systems ........................ 229
D. BIBO Stability of Fuzzy PI+D Control Systems ................... 231
E. Graphical Stability Analysis of Fuzzy PID Control
Systems .................................................................................. 232
Problems............................................................................................. 236
References .......................................................................................... 237
6. Adaptive Fuzzy Control ..................................................................... 239
I. Fundamental Adaptive Fuzzy Control Concept .......................... 240
A. Operational Concepts............................................................. 240
B. System Parameterization........................................................ 242
C. Adjusting Mechanism ............................................................ 243
D. Guidelines for Selecting an Adaptive Fuzzy Controller ........ 245
II. Gain Scheduling .......................................................................... 246
III. Fuzzy Self-Tuning Regulator ...................................................... 252
IV. Model Reference Adaptive Fuzzy Systems................................. 255
V. Dual Control................................................................................ 257
VI. Sub-Optimal Fuzzy Control ........................................................ 258
A. SISO Control Systems ........................................................... 259
B. MIMO Control Systems......................................................... 260
Problems............................................................................................. 266
References .......................................................................................... 269
7. Some Applications of Fuzzy Control ................................................. 271
I. Health Monitoring Fuzzy Diagnostic Systems............................ 271
A. Fuzzy Rule-Based Health Monitoring Expert Systems.......... 272
B. Computer Simulations ........................................................... 276
C. Numerical Results.................................................................. 277
II. Fuzzy Control of Image Sharpness for Autofocus Cameras........ 281
A. Basic Image Processing Techniques ...................................... 282
B. Fuzzy Control Model ............................................................. 283
C. Computer Simulation Results ................................................ 286
III. Fuzzy Control for Servo Mechanic Systems ............................... 291
A. Fuzzy Modeling of a Servo Mechanic System ...................... 292
B. Fuzzy Controller of a Servo Mechanic System...................... 294
C. Computer Simulations and Numerical Results ...................... 295
IV. Fuzzy PID Controllers for Servo Mechanic Systems .................. 300
A. Fuzzy PID Controller of a Servo Mechanic System .............. 300
B. Adaptive Fuzzy Controller of a Servo Mechanic System ...... 301
V. Fuzzy Controller for Robotic Manipulator.................................. 302
A. Fuzzy Modeling of a 2-Link Planar Manipulator................... 304
B. Fuzzy Controller of a 2-Link Planar Manipulator.................. 307
C. Numerical Simulations........................................................... 307
Problems............................................................................................. 311
References .......................................................................................... 313
Index................................................................................................... 315

Download this book click here

Fuzzy Sets and Fuzzy Logic

Download this book click here