Generalized net models of MLNN learning algorithms

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. © Springer-Verlag Berlin Heidelberg 2005.