Architecture Optimization Model of Probabilistic Neural Network

Abstract

Random Probabilistic neural networks are more approximate to humans than determinist neural network.Therefore, it is trivial in our study to use random criterion.There exist several random tools, but the most popular is the Probabilistic Self Organizing Maps.For that reason we chose this latter as a classification tool in this research paper, where we describe, in a first time, our PRSOM model as a MINLP model with linear constraints.And we use the dynamic center method to resolve this model.Then in a second time, we describe our PRSOM model as a MINLP model with nonlinear constraints, that we resolve with the genetic algorithm.In order to validate the theoretical approach, we apply our methods to the domain of classification.Moreover, the results obtained are compared with other classification methods.