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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications : Signals and Communication Technology - Peter Stavroulakis

Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications

Signals and Communication Technology

By: Peter Stavroulakis (Editor)

Hardcover

Published: 24th March 2004
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This highly interdisciplinary book covers for the first time the applications of neuro-fuzzy and fuzzy-neural scientific tools in a very wide area within the communications field. It deals with the important and modern areas of telecommunications amenable to such a treatment. Therefore, it is of interest to researchers and graduate students as well as practising engineers.

List of Contributorsp. XVII
lntroductionp. 1
Integration of Neural and Fuzzyp. 3
Introductionp. 3
Hybrid Artificial Intelligent Systemsp. 6
Neuro-Fuzzy Systemsp. 7
Examples [12-18]p. 27
Referencesp. 38
Neuro-Fuzzy Applications in Speech Coding and Recognitionp. 41
Introductionp. 41
Soft Computingp. 42
FuGeNeSys: A Neuro-Fuzzy Learning Tool for Fuzzy Modelingp. 43
Genetic Algorithmsp. 44
The Fuzzy Inferential Method Adopted and its Codingp. 45
Fuzzy Inference Complexityp. 47
Conventional Speech Coding and Recognition Techniquesp. 47
Speech Recognitionp. 47
Speech Codingp. 53
A Soft Computing-Based Approach in Speech Classificationp. 56
Neuro-Fuzzy Applications in Speech Coding and Recognitionp. 57
Voiced/Unvoiced Classificationp. 57
Voice Activity Detectionp. 60
Endpoint Detectionp. 66
Conclusionsp. 76
Referencesp. 76
Image/Video Compression Using Neuro-Fuzzy Techniquesp. 79
Introductionp. 79
Image Compressionp. 79
Video Compressionp. 80
Fuzzy Theory and Neural Networksp. 81
Neuro-Fuzzy Techniquesp. 82
Fuzzy Kohonen Clustering Networks (FKCN)p. 83
Fuzzy-ART Networksp. 85
Self-Constructing Fuzzy Neural Networks (SCFNN)p. 87
Neuro-Fuzzy Based Vector Quantization for Image Compressionp. 91
VQ Encoding/Decodingp. 92
Clustering by SCFNNp. 93
Experimental Resultsp. 94
Image Transmission by NITFp. 99
Encoding a VQ Compressed NITF Imagep. 99
Decoding a VQ Compressed NITF Imagep. 101
Neuro-Fuzzy Based Video Compressionp. 103
System Overviewp. 104
Clustering by SCFNNp. 105
Labeling Segmentsp. 106
Human Object Estimationp. 107
Human Object Refinementp. 109
Experimental Resultsp. 114
Referencesp. 116
A Neuro-Fuzzy System for Source Location and Tracking in Wireless Communicationsp. 119
Introductionp. 119
Problem Statementp. 120
Signal Modelp. 120
The Periodogram as a Motivational Tool for a Neuro-Fuzzy Systemp. 122
Fuzzy Logic for Model-Free Function Approximationp. 123
The Architecture ofthe Fuzzy-Neural Networkp. 125
Fuzzificationp. 125
Inferencep. 128
Defuzzificationp. 128
Design ofthe Rule Basep. 131
Initializationp. 131
Training ofthe Neuro-Fuzzy Systemp. 133
Back-Propagation Algorithmp. 133
Steps to Follow in the Design ofthe Rule Basep. 137
Simulationsp. 139
Gaussian Fuzzy Setsp. 139
Triangular Fuzzy Setsp. 142
Neuro-Fuzzy System Evaluationp. 142
Referencesp. 147
Fuzzy-Neural Applications in Handoffp. 149
Introductionp. 149
Application ofa Neuro-Fuzzy System to Handoffs in Cellular Communicationsp. 150
Introductionp. 150
Handoff Algorithmsp. 153
Analysis ofHandoffAlgorithmsp. 160
Neural Encoding Based Neuro-Fuzzy Systemp. 163
Pattern Recognition Based Neuro-Fuzzy Systemp. 180
Application ofa Neuro-Fuzzy HandoffApproach to Various Cellular Systemsp. 184
Conclusionp. 196
Referencesp. 197
HandoffBased Quality ofService Control in CDMA Systems Using Neuro-Fuzzy Techniquesp. 198
Introductionp. 198
Classification ofthe Problems and Performance Indicatorsp. 201
An Overview of IS-95A and IS-95B/cdma2000 SHOsp. 202
Simple Step Control Algorithm (SSC)p. 204
FIS SHO and FIS&GD SHOp. 205
System Model, Computer Simulation and Resultsp. 215
Evaluation ofHandoffas a Quality ofService Controllerp. 231
Referencesp. 233
An Application of Neuro-Fuzzy Systems for Access Control in Asynchronous Transfer Mode Networksp. 235
Introductionp. 235
Traffic Control in ATM Networksp. 237
Call Admission Control: CACp. 238
Usage Parameter Control: UPCp. 238
Performance Evaluation ofTraffic Policing Mechanismp. 242
Traffic Source Model and Traffic Policing Mechanismp. 243
Traffic Source Model used in Simulation Testp. 243
Structure ofTraffic Policing Mechanism for Comparisonp. 244
Structure ofTraffic Policing Mechanism Using NFSp. 247
General Problem Statementp. 253
Performance ofFLLB Policing Mechanismp. 254
Effects ofToken Pool Size on Policing Performancep. 254
Performance ofNFS LB Policing Mechanismp. 263
NN Structurep. 264
Simulation Results when Tested with Source Model 1p. 265
Comparison ofProcessing Time ofFL and NFS Controllersp. 279
Evaluation ofSimulation Resultsp. 279
Referencesp. 281
Overview ofNeural Networksp. 283
Introductionp. 283
Learning by Neural Networksp. 285
Multilayer, Feedforward Network Structurep. 285
Training the Feedforward Network: The Delta Rule (DR) and the Generelized Delta Rule (GDR) Back-Propagationp. 286
The Hopfield Approach to Neural Computingp. 287
Unsupervised Classification Learningp. 287
Examples of Neural Network Structures for PR Applicationsp. 289
Neural Network Structurep. 289
Learning in Neural Networksp. 291
Reasons to Adapt a Neural Computational Architecturep. 291
Referencesp. 292
Overview of Fuzzy Logic Systemsp. 293
Introductionp. 293
Overview ofFuzzy Logicp. 293
Fuzzy Rule Generationp. 294
Defuzzification of Fuzzy Logicp. 295
Examplesp. 295
Fuzzy Pattern Recognitionp. 295
Referencesp. 300
Examples ofFuzzy-Neural and Neuro-Fuzzy Integrationp. 301
Fuzzy-Neural Classificationp. 301
Introductionp. 301
Uncertainties with Two-Class Fuzzy-Neural Classification Boundariesp. 306
Multilayer Fuzzy-Neural Classification Networksp. 309
Fuzzy-Neural Clusteringp. 310
Fuzzy Competitive Learning for Fuzzy Clusteringp. 310
Adaptive Fuzzy Leader Clusteringp. 312
Fuzzy-Neural Models for Image Processingp. 315
Fuzzy SelfSupervised Multilayer Network for Object Extractionp. 315
Fuzzy-Neural Networks for Speech Recognitionp. 321
Introductionp. 321
Problem Definitionp. 321
Fuzzy-Neural Approachp. 321
Fuzzy-Neural Hybrid Systems for System Diagnosisp. 323
Introductionp. 323
Hybrid Systemsp. 324
Neuro-Fuzzy Adaptation ofLearning Parameters - An Application in Chromatographyp. 327
Introductionp. 327
Fuzzy Training ofNeural Networksp. 328
Conclusionsp. 334
Referencesp. 335
Subject Indexp. 337
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9783540407591
ISBN-10: 3540407596
Series: Signals and Communication Technology
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 339
Published: 24th March 2004
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.5 x 15.5  x 2.54
Weight (kg): 1.51