Architecture optimization model for the probabilistic self-organizing maps and classification

In the present paper we describe a recent approach of probabilistic self-organizing maps (PRSOM). The PRSOM become more and more interesting in many fields such as: pattern recognition, clustering, classification, speech recognition, data compression, medical diagnosis. The PRSOM give an estimation of the density probability function of the data, this density dependent on the parameters of the PRSOM, such as the architecture. Associated with a given problem, it is one of the most important research problems in the neural network research. Also, we implemented and evaluated the proposed method; the numerical results are powerful and show the practical interest of our approach.