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A Way to Aggregate Multilayer Neural Networks

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

In this paper we consider an aggregation way for multilayer neural networks. For this we will use the generalized nets methodology as well as the index matrix operators. The generalized net methodology was developed as a counterpart of Petri nets for modelling discrete event systems. First, a short introduction of these tools is given. Next, three different kinds of neurons aggregation is considered. The application of the index matrix operators allow to developed three different generalized net models. The methodology seems to be a very good tool for knowledge description.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Maciej Krawczak

  2. Warsaw School of Information Technology, Newelska 6, 01-447, Warsaw, Poland

    Maciej Krawczak

Authors
  1. Maciej Krawczak
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Krawczak, M. (2005). A Way to Aggregate Multilayer Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_4

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  • DOI: https://doi.org/10.1007/11550907_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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