ISBN: 3790814393-c
TITLE: Computational Intelligence Systems and Applications
AUTHOR: Gorzalczany
TOC:

Preface V
1 Introduction 1
1.1 A general concept of computational intelligence 1
1.2 The building blocks of computational intelligence systems 4
1.3 Objectives and scope of this book 13
2 Elements of the theory of fuzzy sets 17
2.1 Basic notions, operations on fuzzy sets, and fuzzy relations 17
2.2 Fuzzy inference systems 35
3 Essentials of artificial neural networks 53
3.1 Processing elements and multilayer perceptrons 54
3.2 Radial basis function networks 74
4 Brief introduction to genetic algorithms 85
4.1 Basic components of genetic algorithms 86
4.2 Theoretical introduction to genetic computing 97
5 Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems 103
5.1 Artificial intelligence versus computational intelligence 103
5.2 Designing computational intelligence systems 108
5.3 Selected neuro-fuzzy systems 115
5.3.1 ANFIS system 115
5.3.2 NEFCLASS system 118
5.3.3 NEFPROX system 121
5.3.4 Neuro-fuzzy system of [242] 123
6 Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data 127
6.1 Synthesizing rule-based knowledge from data - statement of the problem 129
6.2 Neuro-fuzzy system in learning mode - problem of knowledge acquisition 132
6.2.1 Conceptual scheme of the system 132
6.2.2 Implementation of the system 137
6.3 Neuro-fuzzy system in inference mode - approximate inference engine 145
6.3.1 Concept of the system 145
6.3.2 Implementation of the system 146
6.3.3 Testing and pruning the system 154
6.4 Learning techniques 157
6.4.1 Backpropagation-like method 158
6.4.2 Optimization techniques 164
6.4.2.1 Conjugate-gradient algorithm 165
6.4.2.2 Variable-metric algorithm 167
6.4.3 Genetic algorithms 169
6.5 A numerical example of synthesizing rule-based knowledge from data - modelling the Mackey-Glass chaotic time series 170
6.5.1 Designing the neuro-fuzzy model from data 171
6.5.2 A comparative analysis with several alternative modelling techniques 176
6.6 Synthesizing rule-based knowledge from "fish data" 180
6.6.1 Designing the neuro-fuzzy-genetic system from data 181
6.6.2 A comparison with other methodologies 184
7 Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers 191
7.1 System identification - statement of the problem and its general solution in the framework of neuro-fuzzy methodology 193
7.2 Rule-based neuro-fuzzy modelling of an industrial gas furnace system 200
7.2.1 Designing the neuro-fuzzy model of dynamic system from data 200
7.2.2 A comparative analysis with several alternative methodologies 211
7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck 219
7.3.1 Designing the controller from data 219
7.3.2 A comparison of different neuro-fuzzy controllers 224
8 Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support 231
8.1 Designing the classifier from data - statement of the problem 235
8.2 Learning mode of neuro-fuzzy classifier 237
8.2.1 Conceptual scheme of the classifier 237
8.2.2 Implementation of the classifier 240
8.3 Inference (decision making) mode of neuro-fuzzy classifier 247
8.3.1 Concept of the system and its implementation 248
8.3.2 Testing and pruning the system 253
8.4 Neuro-fuzzy decision support system for diagnosing breast cancer 256
8.4.1 Designing the system from data 257
8.4.2 A comparative analysis of several different methodologies applied to diagnosing breast cancer 262
8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science) 267
8.5.1 Designing the system from data 268
8.5.2 A comparative analysis with other techniques for decision support systems design 276
8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology) 278
8.6.1 Designing the system from data 279
8.6.2 A comparative analysis with alternative approaches 286
9 Fuzzy neural network for system modelling and control 289
9.1 Learning mode of the network 290
9.2 Inference mode of the network 295
9.3 Fuzzy neural modelling of dynamic systems (an industrial gas furnace system) 300
9.4 Fuzzy neural controller 306
9.4.1 Structure, learning and operation of the controller 306
9.4.2 A numerical example of fuzzy neural control 312
10 Fuzzy neural classifier 315
10.1 Learning and inference modes of the classifier 315
10.2 Fuzzy neural classifier for diagnosis of surgical cases in the domain of equine colic 322
A Appendices 331
A. 1 Inputs and output of the system of Chapter 6.6
(Fish database) 331
A.1.1 Inputs 331
A.1.2 Output 332
A.2 Inputs and outputs of the system of Chapter 8.4
(Wisconsin Breast Cancer database) 332
A.2.1 Inputs 332
A.2.2 Outputs - set of two class labels 332
A.3 Inputs and outputs of the system of Chapter 8.5
(Glass Identifikation database) 332
A.3.1 Inputs 332
A.3.2 Outputs - set of two class labels 333
A.4 Inputs and outputs of the system of Chapter 8.6
(Abalone database) 333
A.4.1 Inputs 333
A.4.2 Outputs - set of three class labels 333
A.5 Inputs and outputs of the system of Chapter 10.2
(Equine colic database) 334
A.5.1 Inputs 334
A.5.2 Outputs - three sets of class labels 334
References 337
Index 359
END
