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Input Signals Normalization in Kohonen Neural Networks

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

In this paper a Kohonen self-organizing competitive algorithm is considered. A formal approach to classification problem, basing on equivalence relations, is proposed. The Kohonen neural networks are considered as classifying systems. The main topic of this paper is proposal of applying stereographic projection as an input signals normalization procedure. Both theoretical justification is discussed and results of experiments are presented. It turns out that the introduced normalization procedure is effective.

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Authors and Affiliations

  1. Institute of Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, Nawojki 11, 30-072, Kraków, Poland

    Andrzej Bielecki

  2. Department of Geoinformatics and Applied Computer Science, Faculty of Gelogy, Geophysics and Environmental Protection, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Marzena Bielecka

  3. Department of Computer Design and Graphics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Reymonta 4, 30-059, Kraków, Poland

    Anna Chmielowiec

Authors
  1. Andrzej Bielecki
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  2. Marzena Bielecka
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  3. Anna Chmielowiec
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Bielecki, A., Bielecka, M., Chmielowiec, A. (2008). Input Signals Normalization in Kohonen Neural Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_1

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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