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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications

  • Book
  • © 2004

Overview

Editors:
  1. Peter Stavroulakis

    1. Technical University of Crete, Chania, Crete, Greece

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  • First book on neuro-fuzzy applications in the communications area
  • Includes supplementary material: sn.pub/extras

Part of the book series: Signals and Communication Technology (SCT)

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Table of contents (8 chapters)

  1. Front Matter

    Pages I-XVIII
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  2. Introduction

    • Peter Stavroulakis
    Pages 1-2

  3. Integration of Neural and Fuzzy

    • Peter Stavroulakis
    Pages 3-39

  4. Neuro-Fuzzy Applications in Speech Coding and Recognition

    • Francesco Beritelli, Marco Russo, Salvatore Serrano
    Pages 41-78

  5. Image/Video Compression Using Neuro-Fuzzy Techniques

    • Wan-Jui Lee, Chen-Sen Ouyang, Shie-Jue Lee
    Pages 79-118

  6. A Neuro-Fuzzy System for Source Location and Tracking in Wireless Communications

    • Ana Perez-Neira, Joan Bas, Miguel A. Lagunas
    Pages 119-148

  7. Fuzzy-Neural Applications in Handoff

    • Peter Stavroulakis
    Pages 149-234

  8. An Application of Neuro-Fuzzy Systems for Access Control in Asynchronous Transfer Mode Networks

    • Watit Benjapolakul, Aimaschana Niruntasukrat
    Pages 235-281

  9. Appendix A. Overview of Neural Networks

    • Peter Stavroulakis
    Pages 283-335

  10. Back Matter

    Pages 337-339
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Keywords

About this book

Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators.

Editors and Affiliations

  • Technical University of Crete, Chania, Crete, Greece

    Peter Stavroulakis

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