Generalized Net Models of MLNN Learning Algorithms

In this paper we consider generalized net models of learning algorithms for multilayer neural networks. Using the standard backpropagation algorithm we will construct it generalized net model. The methodology seems to be a very good tool for knowledge description of learning algorithms. Next, it will be shown that different learning algorithms have similar knowledge representation – it means very similar generalized net models. The generalized net methodology was developed as a counterpart of Petri nets for modelling discrete event systems. In Appendix, a short introduction is given.

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

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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