Generalized Net Model of Optimization of the Self-Organizing Map Learning Algorithm

Abstract

This paper describes an optimization of the algorithm of self-organizing map neural network. The proposed algorithm takes place during the learning trial. We take into consideration the number of the epochs so their number needs to be decreased. In order to do that, for each epoch the distance from each cluster unit to all training vectors is measured. If the total distance is the same as the distance estimated from the previous epoch, it is assumed that the network is trained and the learning trial stops. The process of optimization is described with the generalized net.