Neuro-fuzzy applications in telecommunications
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Bibliographic Information
Neuro-fuzzy applications in telecommunications
（Signals and communication technology）（Engineering online library）
Springer-Verlag, c2004
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Includes bibliographical references and index
Description and Table of Contents
Description
For the first time, this highly interdisciplinary book covers 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.
Table of Contents
1 Introduction.- 2 Integration of Neural and Fuzzy.- 2.1 Introduction.- 2.2 Hybrid Artificial Intelligent Systems.- 2.2.1 Neuro-Fuzzy Systems.- 2.2.2 Examples [12-18].- References.- 3 Neuro-Fuzzy Applications in Speech Coding and Recognition.- 3.1 Introduction.- 3.2 Soft Computing.- 3.3 FuGeNeSys: A Neuro-Fuzzy Learning Tool for Fuzzy Modeling.- 3.3.1 Genetic Algorithms.- 3.3.2 The Fuzzy Inferential Method Adopted and its Coding.- 3.3.3 Fuzzy Inference Complexity.- 3.4 Conventional Speech Coding and Recognition Techniques.- 3.4.1 Speech Recognition.- 3.4.2 Speech Coding.- 3.5 A Soft Computing-Based Approach in Speech Classification.- 3.6 Neuro-Fuzzy Applications in Speech Coding and Recognition.- 3.6.1 Voiced/Unvoiced Classification.- 3.6.2 Voice Activity Detection.- 3.6.3 Endpoint Detection.- 3.7 Conclusions.- References.- 4 Image/Video Compression Using Neuro-Fuzzy Techniques.- 4.1 Introduction.- 4.1.1 Image Compression.- 4.1.2 Video Compression.- 4.1.3 Fuzzy Theory and Neural Networks.- 4.2 Neuro-Fuzzy Techniques.- 4.2.1 Fuzzy Kohonen Clustering Networks (FKCN).- 4.2.2 Fuzzy-ART Networks.- 4.2.3 Self-Constructing Fuzzy Neural Networks (SCFNN).- 4.3 Neuro-Fuzzy Based Vector Quantization for Image Compression.- 4.3.1 VQ Encoding/Decoding.- 4.3.2 Clustering by SCFNN.- 4.3.3 Experimental Results.- 4.4 Image Transmission by NITF.- 4.4.1 Encoding a VQ Compressed NITF Image.- 4.4.2 Decoding a VQ Compressed NITF Image.- 4.5 Neuro-Fuzzy Based Video Compression.- 4.5.1 System Overview.- 4.5.2 Clustering by SCFNN.- 4.5.3 Labeling Segments.- 4.5.4 Human Object Estimation.- 4.5.5 Human Object Refinement.- 4.5.6 Experimental Results.- References.- 5 A Neuro-Fuzzy System for Source Location and Tracking in Wireless Communications.- 5.1 Introduction.- 5.2 Problem Statement.- 5.2.1 Signal Model.- 5.2.2 The Periodogram as a Motivational Tool for a Neuro-Fuzzy System.- 5.2.3 Fuzzy Logic for Model-Free Function Approximation.- 5.3 The Architecture of the Fuzzy-Neural Network.- 5.3.1 Fuzzification.- 5.3.2 Inference.- 5.3.3 Defuzzification.- 5.4 Design of the Rule Base.- 5.4.1 Initialization.- 5.4.2 Training of the Neuro-Fuzzy System.- 5.4.3 Back-Propagation Algorithm.- 5.4.4 Steps to Follow in the Design of the Rule Base.- 5.5 Simulations.- 5.5.1 Gaussian Fuzzy Sets.- 5.5.2 Triangular Fuzzy Sets.- 5.6 Neuro-Fuzzy System Evaluation.- References.- 6 Fuzzy-Neural Applications in Handoff.- 6.1 Introduction.- 6.2 Application of a Neuro-Fuzzy System to Handoffs in Cellular Communications.- 6.2.1 Introduction.- 6.2.2 Handoff Algorithms.- 6.2.3 Analysis of Handoff Algorithms.- 6.2.4 Neural Encoding Based Neuro-Fuzzy System.- 6.2.5 Pattern Recognition Based Neuro-Fuzzy System.- 6.2.6 Application of a Neuro-Fuzzy Handoff Approach to Various Cellular Systems.- 6.2.7 Conclusion.- References.- 6.3 Handoff Based Quality of Service Control in CDMA Systems Using Neuro-Fuzzy Techniques Bongkarn Homnan, Watit Benjapolakul.- 6.3.1 Introduction.- 6.3.2 Classification of the Problems and Performance Indicators.- 6.3.3 An Overview of IS-95A and IS-95B/cdma2000 SHOs.- 6.3.4 Simple Step Control Algorithm (SSC).- 6.3.5 FIS SHO and FIS&GD SHO.- 6.3.6 System Model, Computer Simulation and Results.- 6.3.7 Evaluation of Handoff as a Quality of Service Controller.- References.- 7 An Application of Neuro-Fuzzy Systems for Access Control in Asynchronous Transfer Mode Networks.- 7.1 Introduction.- 7.2 Traffic Control in ATM Networks.- 7.2.1 Call Admission Control: CAC.- 7.2.2 Usage Parameter Control: UPC.- 7.2.3 Performance Evaluation of Traffic Policing Mechanism.- 7.3 Traffic Source Model and Traffic Policing Mechanism.- 7.3.1 Traffic Source Model used in Simulation Test.- 7.3.2 Structure of Traffic Policing Mechanism for Comparison.- 7.3.3 Structure of Traffic Policing Mechanism Using NFS.- 7.3.4 General Problem Statement.- 7.4 Performance of FLLB Policing Mechanism.- 7.4.1 Effects of Token Pool Size on Policing Performance.- 7.5 Performance of NFS LB Policing Mechanism.- 7.5.1 NN Structure.- 7.5.2 Simulation Results when Tested with Source Model 1.- 7.5.3 Comparison of Processing Time of FL and NFS Controllers.- 7.6 Evaluation of Simulation Results.- References.- Appendix A. Overview of Neural Networks.- A.1 Introduction.- A.2 Learning by Neural Networks.- A.2.1 Multilayer, Feedforward Network Structure.- A.2.2 Training the Feedforward Network: The Delta Rule (DR) and the Generelized Delta Rule (GDR) Back-Propagation.- A.2.3 The Hopfield Approach to Neural Computing.- A.2.4 Unsupervised Classification Learning.- A.3 Examples of Neural Network Structures for PR Applications.- A.3.1 Neural Network Structure.- A.3.2 Learning in Neural Networks.- A.3.3 Reasons to Adapt a Neural Computational Architecture.- References.- Appendix B. Overview of Fuzzy Logic Systems.- B.1 Introduction.- B.2 Overview of Fuzzy Logic.- B.2.1 Fuzzy Rule Generation.- B.2.2 Defuzzification of Fuzzy Logic.- B.3 Examples.- B3.1 Fuzzy Pattern Recognition.- References.- Appendix C. Examples of Fuzzy-Neural and Neuro-Fuzzy Integration.- C.1 Fuzzy-Neural Classification.- C.1.1 Introduction.- C.1.2 Uncertainties with Two-Class Fuzzy-Neural Classification Boundaries.- C.1.3 Multilayer Fuzzy-Neural Classification Networks.- C.2 Fuzzy-Neural Clustering.- C.2.1 Fuzzy Competitive Learning for Fuzzy Clustering.- C.2.2 Adaptive Fuzzy Leader Clustering.- C.3 Fuzzy-Neural Models for Image Processing.- C.3.1 Fuzzy Self Supervised Multilayer Network for Object Extraction.- C.4 Fuzzy-Neural Networks for Speech Recognition.- C.4.1 Introduction.- C.4.2 Problem Definition.- C.4.3 Fuzzy-Neural Approach.- C.5 Fuzzy-Neural Hybrid Systems for System Diagnosis.- C.5.1 Introduction.- C.5.2 Hybrid Systems.- C.6 Neuro-Fuzzy Adaptation of Learning Parameters - An Application in Chromatography.- C.6.1 Introduction.- C.6.2 Fuzzy Training of Neural Networks.- C.6.3 Conclusions.- References.
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