Abstract
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.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Krawczak, M. (2005). Generalized Net Models of MLNN Learning Algorithms. 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_5
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DOI: https://doi.org/10.1007/11550907_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
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