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Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Roanne, France, 2003

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. David W. Pearson

    1. Equipe Universitaire de Recherche en Informatique de Saint-Etienne (Groupe de Recherche de Roanne) Institut Universitaire de Technologie de Roanne, Université Jean Monnet, Saint-Etienne, France

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  2. Nigel C. Steele

    1. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

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  3. Rudolf F. Albrecht

    1. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

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  • Latest developments in neural nets and genetic algorithms

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Table of contents (47 papers)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. Validation of a RBFN Model by Sensitivity Analysis

    • Özer Ciftcioglu
    Pages 1-8

  3. Generalized recurrent neural networks and continuous dynamic systems

    • Ivan Gabrijel, Andrej Dobnikar
    Pages 9-12

  4. A learning probabilistic neural network with fuzzy inference

    • Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, J. Wernstedt
    Pages 13-17

  5. Boosting Recurrent Neural Networks for Time Series Prediction

    • R. Boné, M. Assaad, M. Crucianu
    Pages 18-22

  6. Bounds on Rates of Approximation by Neural Networks in L p -spaces

    • Terezie Šidlofová
    Pages 23-27

  7. On input discretisation processes for tapped delay neural architecture

    • Bartłomiej Beliczynski
    Pages 28-32

  8. An extended architecture of recurrent neural networks that latches input information

    • Branko Šter, Andrej Dobnikar
    Pages 33-37

  9. Recurrent neural network with integrated wavelet based denoising unit

    • Uroš Lotrič, Andrej Dobnikar
    Pages 38-42

  10. Reinforced Search in Stochastic Neural Network

    • Mira Trebar, Andrej Dobnikar
    Pages 43-46

  11. A hybrid algorithm for weight and connectivity optimization in feedforward neural networks

    • F. Pettersson, H. Saxén
    Pages 47-52

  12. Neural network learning as approximate optimization

    • Věra Kůrková, Marcello Sanguineti
    Pages 53-57

  13. Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression

    • A. A. Frolov, A. M. Sirota, D. Husek, I. Muraviev, P. Combe
    Pages 58-62

  14. β_SVM a new Support Vector Machine kernel

    • Tarek M. Hamdani, Adel M. Alimi
    Pages 63-68

  15. Optimal neighbourhood and model quality indicators

    • Stefan Janaqi, François Hartmann, Meriam Chebre, Edith di Crescenzo
    Pages 69-73

  16. Manufacturing process quality control by means of a Fuzzy ART neural network algorithm

    • Massimo Pacella, Quirico Semeraro, Alfredo Anglani
    Pages 74-79

  17. Hybrid Models for Forecasting Air Pollution Episodes

    • Harri Niska, Teri Hiltunen, Mikko Kolehmainen, Juhani Ruuskanen
    Pages 80-84

  18. Influence of Language Parameters Selection on the Coarticulation of the Phonemes for Prosody Training in TTS by Neural Networks

    • Jana Tučková, Václav Šebesta
    Pages 85-90

  19. Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting

    • David William Pearson, Mireille Batton-Hubert, Gérard Dray
    Pages 91-94

  20. A systematic method to neural network modeling with application to sintermaking

    • Petteri Laitinen, Henrik Saxén
    Pages 95-100
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Keywords

About this book

The 2003 edition of ICANNGA marks a milestone in this conference series, because it is the tenth year of its existence. The series began in 1993 with the inaugural conference at Innsbruck in Austria. At that first conference, the organisers decided to organise a similar scientific meeting every two years. As a result, conferences were organised at Ales in France (1995), Norwich in England (1997), Portoroz in Slovenia (1999) and Prague in the Czech Republic (2001). It is a great honour that the conference is taking place in France for the second time. Each edition of ICANNGA has been special and had its own character. Not only that, participants have been able to sample the life and local culture in five different European coun­ tries. Originally limited to neural networks and genetic algorithms the conference has broadened its outlook over the past ten years and now includes papers on soft computing and artificial intelligence in general. This is one of the reasons why the reader will find papers on fuzzy logic and various other topics not directly related to neural networks or genetic algorithms included in these proceedings. We have, however, kept the same name, "International Conference on Artificial Neural Networks and Genetic Algorithms". All of the papers were sorted into one of six principal categories: neural network theory, neural network applications, genetic algorithm and evolutionary computation theory, genetic algorithm and evolutionary computation applications, fuzzy and soft computing theory, fuzzy and soft computing applications.

Editors and Affiliations

  • Equipe Universitaire de Recherche en Informatique de Saint-Etienne (Groupe de Recherche de Roanne) Institut Universitaire de Technologie de Roanne, Université Jean Monnet, Saint-Etienne, France

    David W. Pearson

  • Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

  • Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

Bibliographic Information

  • Book Title: Artificial Neural Nets and Genetic Algorithms

  • Book Subtitle: Proceedings of the International Conference in Roanne, France, 2003

  • Editors: David W. Pearson, Nigel C. Steele, Rudolf F. Albrecht

  • DOI: https://doi.org/10.1007/978-3-7091-0646-4

  • Publisher: Springer Vienna

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Wien 2003

  • Softcover ISBN: 978-3-211-00743-3Published: 08 April 2003

  • eBook ISBN: 978-3-7091-0646-4Published: 28 June 2011

  • Edition Number: 1

  • Number of Pages: X, 266

  • Number of Illustrations: 48 b/w illustrations

  • Topics: Artificial Intelligence, Complexity

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