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Fuzzy Learning and Applications : International Series on Computational Intelligence - Lakhmi C. Jain

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Fuzzy Learning and Applications

Name: Fuzzy Learning and Applications
Brand: CRC PR INC
Price: 154.25 AUD
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By: Lakhmi C. Jain, Marco Russo

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Hardcover | 13 December 2000 | Edition Number 1

At a Glance

Hardcover
404 Pages

Dimensions(cm)
23.4 x 15.6 x 2.41

Hardcover

RRP $166.00

$154.25

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With low computational complexity and relatively short development time, Fuzzy Logic is an indispensable tool for engineering applications. The field is growing at an unprecedented rate, and there is a need for a book that describes essential tools, applications, examples, and perspectives in the field of fuzzy learning. The editors of Fuzzy Learning and Applications fill this need, providing an essential book for researchers, scientists, and engineers alike.

Organized into four parts, this book starts with the simplest learning method and gradually arrives at the most complex. First, it summarizes all the symbols and formulae used in the succeeding chapters and presents a historical overview of fuzzy learning. Next, it deals with current techniques, ranging from deterministic to hybrid methods. It then illustrates the enormous number of possibilities offered by fuzzy learning. Finally, it covers hardware dedicated to fuzzy learning, from digital to analog designs and implementations. With Fuzzy Learning and Applications, readers will discover the enormous possibilities fuzzy learning offers.

Industry Reviews

"This book would be an excellent resource for graduate students and advanced researchers in two ways: it would enable them to learn about some of the advanced research and applications being carried out, and would provide them with a variety of ideas for further research and applications." -R. Bharath, CHOICE, September 2001

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Non-Fiction Computing & I.T.Computer Science Systems Analysis & Design Mathematics Engineering & Technology Electronics & Communications Engineering Electronics Engineering Artificial Intelligence Machine Learning Mechanical Engineering & Materials Mechanical Engineering

Evolutionary Fuzzy Learning
Abstract	p. 2
Introduction	p. 2
Fuzzy knowledge representation	p. 2
Gefrex	p. 5
General description
The evolution algorithm
Genetic coding
Crossover
The error
Output singletons
Hill climbing operator
The fitness function
Gefrex utilization
Comparisons
Computational complexity evaluation
A learning example with compressed learning patterns
Pargefrex	p. 33
A brief review of Gefrex
The commodity supercomputer used
Pargefrex description
Performance evaluation
References	p. 48
A Stored-Programmable Mixed-Signal Fuzzy Controller Chip with Supervised Learning Capabilities
Abstract	p. 52
Introduction	p. 52
Architecture and Functional Description	p. 54
Inference Procedure
Non-multiplexed Architecture
Multiplexed Architecture
Analog Core Implementation	p. 60
Non-Multiplexed Building Blocks
Modifications for the Multiplexed Architecture
A/D Converters, Interval Selector and Digital Memory	p. 75
A/D Converters
Interval Selector
Digital Memory
Learning Capability	p. 80
Program Approach
Learning Approach
Results and Conclusions	p. 85
Acknowledgement	p. 89
References	p. 89
Fuzzy Modeling in a Multi-Agent Framework for Learning in Autonomous Systems
Abstract	p. 94
Introduction	p. 94
Fuzzy Modeling and Agents	p. 95
Distributed Artificial Intelligence(DAI)
Tasks in MAS Development
An Agentoriented Methodology
MAST. The Multi-agent System Toolbox
The MIX Agent model
MAST: the Tool for Developing Multi-Agent Systems
Exchanging Information Objects with MAST
A MAS Architecture for Fuzzy Modeling (MASAM)	p. 101
Agents Architecture
Fuzzy Modeling in our MAS Architecture
Fuzzy Clustering: a Central Component for Fuzzy Modeling
Clustering Agents	p. 109
Fuzzy LVQ
Fuzzy Tabu Clustering
Rule Generation Agents
A Genetic Algorithm Based Method to Generate and/or Tune Fuzzy Rules
Robotics Application Example	p. 117
Introduction
The BG programming language
Robot agents architecture
Learning behaviour fusion
Experimental results
Conclusions and Future Work	p. 133
References	p. 141
Learning Techniques for Supervised Fuzzy Classifiers
Abstract	p. 148
Introduction	p. 148
The Fuzzy Basis Function Network	p. 149
Bayes Optimal Classifier Approximation	p. 152
Learning in a FBFN Classifier	p. 154
Data Base and Preprocessing	p. 155
Classification Performances	p. 156
FBFN Structure Identification and Semantic Phase Transition	p. 158
The Simplified FBF Network and Its Extension	p. 159
Performance of the SFBF and ESFBF networks	p. 160
Hybrid Network	p. 162
Conclusions	p. 165
Acknowledgments	p. 166
References	p. 167
Multistage Fuzzy Control
Abstract	p. 172
Introduction	p. 172
Multistage fuzzy systems
Related studies and problems
Multistage approach
Multistage inference fuzzy systems	p. 179
Multistage fuzzy inference engine
Multistage fuzzy inference procedure
Methodology of fuzzy rule generation	p. 183
Fuzzy rule generation for multi-stage fuzzy inference systems
Fast multi-stage fuzzy logic inference
An illustrative example	p. 191
Conclusion	p. 199
References	p. 202
Learning Fuzzy Systems
Abstract	p. 206
Introduction	p. 206
Fuzzy Systems	p. 207
Fuzzy Systems
Learning Fuzzy Systems	p. 211
History
Neural-fuzzy Systems
Neuro-fuzzy Systems
Parameter Adjustment
Learning Rule	p. 214
Learning Fuzzy Systems
Interpretation Preservation	p. 216
Constraint Learning
Constraint Learning Rule
Interpretation Preservation of Learning Fuzzy Systems
Conclusions	p. 220
References	p. 221
An Application of Fuzzy Modeling to Analysis of Rowing Boat Speed
Abstract	p. 224
Introduction	p. 224
Complexities in rowing	p. 225
Fuzzy modeling	p. 226
Fuzzy neural network
Uneven division of input space
Experiments	p. 231
Modeling results	p. 235
Conclusion	p. 236
References	p. 240
A Novel Fuzzy Approach to Hopfield Coefficients Determination
Abstract	p. 242
Introduction	p. 242
Hopfield-Type Neural Network	p. 244
Fuzzy Logic	p. 245
The Fuzzy Tuning of Hopfield Coefficients	p. 248
A Detailed Description of the Algorithm for Coefficient Determination
Membership Function Tuning
Examples of Application of the Proposed Method	p. 258
The Traveling Salesman Problem
Flexible Manufacturing System Performance Optimization
Remarks on the Tuning of the Parameters	p. 267
Description of the Fuzzy Inferences trained	p. 268
Fuzzy Approach versus Heuristic Determination of HCs	p. 270
TSP by Hopfield and Tank's Original Energy Function
TSP by Szu's Energy Function
FMS Performance Optimization
Conclusions	p. 275
References	p. 276
Fuzzy control of a CD player focusing system
Abstract	p. 280
Introduction	p. 280
The CD player	p. 281
System identification	p. 284
Traditional controller synthesis	p. 285
Optimized fuzzy controller: the direct method	p. 287
The indirect optimization strategy	p. 290
Approximation of the classical controller by a set of fuzzy rules
Optimization of the fuzzy controller
Improvements introduced by the fuzzy controller	p. 295
Implementation details	p. 299
Conclusion	p. 301
References	p. 302
A Neuro-Fuzzy Scheduler for a Multimedia Web Server
Abstract	p. 306
Introduction	p. 306
Quality and Synchronization Requirement of Multimedia Information	p. 311
Synchronization Requirements
QOP Requirements
Synchronization in a Multimedia Web Environment	p. 314
Non-stationary Work-Load
Dynamic Bandwidth and Resource Constraints
AUS Filtering Process
Interval Based Dynamic Scheduling
Work-Load Characterization	p. 320
Dynamic Scheduling at the Server	p. 322
A Multi-criteria Scheduling Problem
Computation Complexity of the Multi-criteria Scheduling Problem
The Proposed Neuro-Fuzzy Scheduler	p. 328
Hybrid Learning Algorithm
NFS Heuristics
Performance Evaluation	p. 338
The Learned Fuzzy Logic Rules
Learned Membership Functions
Performance Results
Conclusion	p. 350
Appendix	p. 351
References	p. 355
A Neuro-Fuzzy System Based on Logical Interpretation of If-Then Rules
Abstract	p. 360
Introduction	p. 360
An approach to axiomatic definition of fuzzy implication	p. 362
Reasoning using fuzzy implications and generalized modus ponens	p. 369
Fundamentals of fuzzy systems	p. 372
Fuzzy system with logical interpretation of if-then rules	p. 375
Application of ANBLIR to pattern recognition	p. 381
Numerical examples	p. 382
Application to forensic glass classification
Application to the famous iris problem
Application to wine recognition data
Application to MONKS problems
Conclusions	p. 386
References	p. 387
Table of Contents provided by Syndetics. All Rights Reserved.

Fuzzy Learning and Applications

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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