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E-raamat: Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases [World Scientific e-raamat]

(Univ Of Granada, Spain), (Royal Ins Of Tech, Stockholm), (Univ Of Granada, Spain), (Univ Politecnica De Madrid, Spain)
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Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge BasesSuurem pilt
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Raamatu kodulehekülg: http://www.worldscientific.com/worldscibooks/10.1142/4177#t=toc
In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
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
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