Self-Organizing Neural Networks

Recent Advances and Applications

  • Udo Seiffert
  • Lakhmi C. Jain

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 78)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Teuvo Kohonen
    Pages 1-12
  3. Thomas Natschläger, Berthold Ruf, Michael Schmitt
    Pages 45-73
  4. Jouko Lampinen, Timo Kostiainen
    Pages 75-94
  5. Friedhelm Schwenker, Hans A. Kestler, Günther Palm
    Pages 165-183
  6. Friedhelm Schwenker, Hans A. Kestler, Günther Palm
    Pages 217-243
  7. Timo D. Hämäläinen
    Pages 245-278

About this book


The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna­ tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad­ equate. It is rather the universal applicability and easy handling of the SOM. Com­ pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never­ theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the­ oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up­ to-date treatment of the field of self-organizing neural networks, which will be ac­ cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup­ porting this book and contributing the first chapter.


Extension Kohonen Map algorithms artificial intelligence artificial neural network cognition hardware image analysis learning modeling neural networks self-organizing map speech processing spiking neurons supervised learning

Editors and affiliations

  • Udo Seiffert
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.Faculty of Electrical Engineering, Technical Computer Science GroupUniversity of MagdeburgMagdeburgGermany
  2. 2.Knowledge-Based Intelligent, Engineering Systems CentreUniversity of South AustraliaAdelaideAustralia

Bibliographic information

  • DOI
  • Copyright Information Physica-Verlag Heidelberg 2002
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-00343-5
  • Online ISBN 978-3-7908-1810-9
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site