Thursday 28th of March 2024
 

Architecture Optimization Model of Probabilistic Neural Network


Zakariae En-Naimani and Mohamed Ettaouil

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.

Keywords: Neural Random Network, self-organization map, classification, unsupervised learning, MINLP model.

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ABOUT THE AUTHORS

Zakariae En-Naimani
PhD Student

Mohamed Ettaouil
PhD Professor


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