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Tables of Contents for Genetic Fuzzy Systems
Chapter/Section Title
Page #
Page Count
Foreword
vii
Preface
xi
Fuzzy Fule-Based Systems
1
46
Framework: Fuzzy Logic and Fuzzy Systems
2
1
Mamdani Fuzzy Rule-Based Systems
3
17
The knowledge base of Mamdani fuzzy rule-based systems
4
2
The inference engine of Mamdani fuzzy rule-based systems
6
1
The fuzzification interface
7
1
The inference system
7
1
The defuzzification interface
8
2
Example of application
10
4
Design of the inference engine
14
1
Advantages and drawbacks of Mamdani-type fuzzy rule-based systems
15
2
Variants of Mamdani fuzzy rule-based systems
17
1
DNF Mamdani fuzzy rule-based systems
17
1
Approximate Mamdani-type fuzzy rule-based systems
18
2
Takagi-Sugeno-Kang Fuzzy Rule-Based Systems
20
2
Generation of the Fuzzy Rule Set
22
11
Design tasks for obtaining the fuzzy rule set
22
2
Kinds of information available to define the fuzzy rule set
24
1
Generation of linguistic rules
25
2
Generation of approximate Mamdani-type fuzzy rules
27
3
Generation of TSK fuzzy rules
30
1
Basic properties of fuzzy rule sets
30
1
Completeness of a fuzzy rule set
30
1
Consistency of a fuzzy rule set
31
1
Low complexity of a fuzzy rule set
32
1
Redundancy of a fuzzy rule set
32
1
Applying Fuzzy Rule-Based Systems
33
14
Fuzzy modelling
33
1
Benefits of using fuzzy rule-based systems for modelling
33
2
Relationship between fuzzy modelling and system identification
35
1
Some applications of fuzzy modelling
36
1
Fuzzy control
37
1
Advantages of fuzzy logic controllers
37
2
Differences between the design of fuzzy logic controllers and fuzzy models
39
1
Some applications of fuzzy control
40
1
Fuzzy classification
40
1
Advantages of using fuzzy rule-based systems for classification
40
1
Components and design of fuzzy rule-based classification systems
41
4
Some applications of fuzzy classification
45
2
Evolutionary Computation
47
32
Conceptual Foundations of Evolutionary Computation
47
3
Genetic Algorithms
50
19
Main characteristics
51
7
Schema theorem
58
2
Extensions to the simple genetic algorithm
60
1
Genetic encoding of solutions
60
1
Fitness scaling
61
1
Selection and replacement schemes
62
1
Niching genetic algorithms
63
1
Recombination
64
1
Real-coded genetic algorithms
64
1
Recombination in real-coded genetic algorithms
65
1
Mutation in real-coded genetic algorithms
66
1
Messy genetic algorithms
66
1
Under- and over-specification
67
1
Genetic operators
68
1
Other Evolutionary Algorithms
69
10
Evolution strategies
69
4
Evolutionary programming
73
1
Genetic programming
74
5
Introduction to Genetic Fuzzy Systems
79
20
Soft Computing
79
1
Hybridisation in Soft Computing
80
6
Fuzzy logic and neural networks
80
2
Neuro-fuzzy systems
82
1
Fuzzy-neural networks
83
1
Neural networks and evolutionary computation
84
1
Genetic algorithms for training neural networks
84
1
Genetic algorithms for learning the topology of the network
85
1
Genetic algorithms and probabilistic reasoning
85
1
Integration of Evolutionary Algorithms and Fuzzy Logic
86
3
Fuzzy evolutionary algorithms
86
1
Adaptation of genetic algorithm control parameters
87
1
Genetic algorithm components based on fuzzy tools
88
1
Genetic Fuzzy Systems
89
10
Genetic fuzzy rule-based systems
89
1
Defining the phenotype space for a genetic fuzzy rulebased system
90
2
Genetic tuning of the data base
92
1
Genetic learning of the rule base
93
1
Genetic learning of the knowledge base
93
1
A phenotype space of rules or rule bases/knowledge bases
93
1
From phenotype to genotype spaces
94
1
Generating new genetic material
95
1
Evaluating the genetic material
95
1
The cooperation versus competition problem
96
3
Genetic Tuning Processes
99
28
Tuning of Fuzzy Rule-Based Systems
100
9
Tuning of scaling functions
100
1
Linear contexts
101
2
Non-linear contexts
103
4
Tuning of membership functions
107
1
Tuning of fuzzy rules
108
1
Genetic Turning of Scaling Functions
109
1
Gnetic Tuning of Membership Functions of Mamdani Fuzzy Rule-Based Systems
110
8
Shape of the membership functions
110
1
Piece-wise linear functions
111
1
Differentiable functions
112
1
Scope of the semantics
112
1
Tuning of descriptive Mamdani fuzzy rule-based systems
112
2
Tuning of approximate Mamdani fuzzy rule-based systems
114
1
Example: genetic tuning processes of Mamdani-type fuzzy rule sets
115
1
Common aspects of both genetic tuning processes
115
1
The approximate genetic tuning process
116
2
The descriptive genetic tuning process
118
1
Genetic Tuning of TSK Fuzzy Rule Sets
118
9
Genetic tuning of TSK rule consequent parameters
119
2
Example: the evolutionary tuning process of MOGUL for TSK knowledge bases
121
1
Representation
121
1
Initial population
122
1
Genetic operators
123
4
Learning with Genetic Algorithms
127
26
Genetic Learning Processes. Introduction
127
3
The Michigan Approach. Classifier Systems
130
11
The performance system
131
2
The credit assignment system
133
3
The classifier discovery system
136
1
Basic operations of a classifier system
137
1
Classifier system extensions
138
1
ZCS and XCS
138
3
Anticipatory Classifier System
141
1
The Pittsburgh Approach
141
7
The pupulation of rule bases
143
1
The evaluation system
144
1
The rule base discovery system
144
1
Representation
145
1
Genetic learning processes based on the Pittsburgh approach
145
1
GABIL learning system
146
1
GIL learning system
146
1
Corcoran and Sen's learning system
147
1
The Iterative Rule Learning Approach
148
5
Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach
153
26
Basic Features of Fuzzy Classifier Systems
154
4
Fuzzy Classifier Systems for Learning Rule Bases
158
11
Valenzuela-Rendon's FCS: Introducing reinforcement learning
161
2
Fuzzy classifier systems for learning fuzzy classification rules
163
1
Coding the linguistic classification rules and initial population
163
1
Evaluation of each rule
164
1
Genetic operations for generating new rules
165
1
Rule replacement and termination test
166
1
Algorithm
166
1
Fuzzy classifier system for learning classification rules: extensions
167
2
Fuzzy Classifier Systems for Learning Fuzzy Rule Bases
169
10
Parodi and Bonelli's fuzzy classifier system for approximate fuzzy rule bases
169
1
Basic model
169
1
Description of the algorithm
170
2
Fuzzy classifier system for on-line learning of approximate fuzzy control rules
172
1
The proposed fuzzy classifier system
172
5
On-line learning of fuzzy rules using the Limbo
177
2
Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach
179
40
Coding Rule Bases as Chromosomes
180
24
Positional semantics
181
1
Decision tables
181
2
Relational matrices
183
1
TSK-type rules
184
1
Non-positional semantics (list of rules)
185
2
Rules of fixed length
187
11
Rules of variable length
198
5
Rules of approximate type
203
1
Multi-chromosome Genomes (Coding Knowledge Bases)
204
4
Representation
205
1
Operators
206
2
Examples
208
11
A method to learn decision tables
208
1
A method to learn relational matrices
209
1
A method to learn TSK-type knowledge bases
210
2
A method to learn DNF Mamdani-type knowledge bases (with rules of fixed length)
212
2
A method to learn DNF Mamdani-type rule bases (with rules of variable length)
214
3
A method to learn approximate Mamdani-type fuzzy rule bases
217
2
Genetic Fuzzy Rule-Based Systems Based on the Interative Rule Learning Approach
219
46
Coding the Fuzzy Rules
221
4
Coding linguistic rules
221
2
Coding approximate Mamdani-type fuzzy rules
223
2
Coding TSK fuzzy rules
225
1
Learning Fuzzy Rules under Competition
225
19
The fuzzy rule generating method
226
1
Different criteria for the fitness function of the generating method
226
5
Some examples of fuzzy rule generating methods
231
8
The iterative covering method
239
1
The iterative covering method of MOGUL
240
1
The iterative covering method of SLAVE
241
3
Post-Processing: Refining Rule Bases under Cooperation
244
5
The post-processing algorithm of MOGUL
244
2
The basic genetic simplification process
246
1
The multi-simplification process
246
1
The post-processing algorithm of Slave
247
2
Inducing Cooperation in the Fuzzy Rule Generation Stage
249
9
Inducing cooperation in descriptive fuzzy rule generation processes: the proposals of Slave
249
1
Cooperation between rules in Slave for crisp consequent domain problems
250
3
Cooperation between rules in Slave for fuzzy consequent domain problems
253
1
Inducing cooperation in approximate fuzzy rule generation processes: the low niche interaction rate considered in MOGUL
254
3
Inducing cooperation in TSK fuzzy rule generation processes: the local error measure considered in MOGUL
257
1
Examples
258
7
MOGUL
258
4
Slave
262
3
Other Genetic Fuzzy Rule-Based System Paradigms
265
68
Designing Fuzzy Rule-Based Systems with Genetic Programming
265
8
Learning rule bases with genetic programming
266
1
Encoding rule bases
266
2
Necessity of a typed-system
268
1
Generating rule bases
269
2
Learning knowledge bases with genetic programming
271
2
Genetic Selection of Fuzzy Rule Sets
273
22
Genetic selection from a set of candidate fuzzy rules
275
3
Genetic selection of rule bases integrating linguistic modifiers to change the membership function shapes
278
1
The use of linguistic modifiers to adapt the membership function shapes
278
1
A genetic multi-selection process considering linguistic hedges
279
1
The basic genetic selection method
280
3
Algorithm of the genetic multi-selection process
283
1
Parametric extension
283
1
ALM: Accurate linguistic modelling using genetic selection
284
1
Some important remarks about ALM
285
1
A linguistic modelling process based on ALM
286
1
Genetic selection with hierarchical knowledge bases
287
1
Hierarchical knowledge base philosophy
287
5
System modelling with hierarchical knowledge bases
292
3
Learning the Knowledge Base via the Genetic Derivation of the Data Base
295
38
Learning the knowledge base by deriving the data base
295
3
Genetic learning of membership functions
298
1
Genetic learning of isosceles triangular-shaped membership functions
298
2
Genetic learning of membership functions with implicit granularity learning
300
3
Genetic learning of the granularity and the membership functions
303
5
Genetic algorithm-based fuzzy partition learning method for pattern classification problems
308
4
Genetic learning of non-linear contexts
312
1
Genetic learning process for the scaling factors, granularity and non-linear contexts
312
4
Other Genetic-Based Machine Learning Approaches
316
1
Genetic integration of multiple knowledge bases
316
2
Flexibility, completeness, consistency, compactness and complexity reduction
318
1
Evolutionary generation of flexible, complete, consistent and compact fuzzy rule-based systems
318
1
Genetic complexity reduction and interpretability improvement
319
1
Hierarchical distributed genetic algorithms: designing fuzzy rule-based systems using a multi-resolution search paradigm
320
1
Parallel genetic algorithm to learn knowledge bases with different granularity levels
321
1
Modular and hierarchical evolutionary design of fuzzy rule-based systems
322
1
VEGA: virus-evolutionary genetic algorithm to learn TSK fuzzy rule sets
323
1
Preliminaries: virus theory of evolution
324
1
Virus-evolutionary genetic algorithm architecture
324
1
Virus infection operators
325
1
The VEGA genetic fuzzy rule-based system
326
2
Nagoya approach: genetic-based machine learning algorithm using mechanisms of genetic recombination in bacteria genetics
328
1
Bacterial genetics
329
1
Nagoya approach: algorithm description
329
1
Nagoya approach: extensions
330
1
Learning fuzzy rules with the use of DNA coding
331
1
Hybrid fuzzy genetic-based machine learning algorithm (Pittsburgh and Michigan) to designing compact fuzzy rule-based systems
331
2
Other Kinds of Evolutionary Fuzzy Systems
333
42
Genetic Fuzzy Neural Networks
333
8
Genetic learning of fuzzy weights
334
2
Genetic learning of radial basis functions and weights
336
2
Genetic learning of fuzzy rules through the connection weights
338
2
Combination of genetic algorithms and delta rule for coarse and fine tuning of a fuzzy-neural network
340
1
Genetic Fuzzy Clustering
341
22
Introduction to the clustering problem
341
2
Hard clustering
343
1
Fuzzy clustering
344
7
Different applications of evolutionary algorithms to fuzzy clustering
351
2
Prototype-based genetic fuzzy clustering
353
3
Fuzzy partition-based genetic fuzzy clustering
356
2
Genetic fuzzy clustering by defining the distance norm
358
1
Pure genetic fuzzy clustering
359
4
Genetic Fuzzy Decision Trees
363
12
Decision trees
363
3
Fuzzy decision trees
366
5
Optimising Fuzzy Decision Trees
371
4
Applications
375
50
Classification
375
7
Genetic fuzzy rule-based systems to learn fuzzy classification rules: revision
375
1
Diagnosis of myocardial infarction
376
1
Breast cancer diagnosis
377
1
Fuzzy rule-based classification system parameters
378
2
Genetic learning approach
380
1
Results
381
1
System Modelling
382
17
Power distribution problems in Spain
383
2
Computing the length of low voltage lines
385
6
Computing the maintenance costs of medium voltage line
391
3
The rice taste evaluation problem
394
3
Dental development age prediction
397
2
Control Systems
399
14
The cart-pole balancing system
401
1
Goal and fitness function
401
1
Some results
402
3
A diversification problem
405
4
Supervision of fossil power plant operation
409
1
Problem statement
409
1
Genetic fuzzy rule-based system
410
1
Application results
411
2
Robotics
413
12
Behaviour-based robotics
413
2
Evolutionary robotics
415
1
Robotic platform
416
1
Perception of the environment
417
2
Fuzzy logic controller
419
1
Genetic fuzzy rule-based system
420
5
Bibliography
425
32
Acronyms
457
2
Index
459