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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]