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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2016
    • vol. 4, nº 1, september 2016
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2016
    • vol. 4, nº 1, september 2016
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    Multilayer Perceptron: Architecture Optimization and Training

    Autor: 
    Ramchoun, Hassan
    ;
    Ghanou, Youssef
    ;
    Ettaouil, Mohamed
    ;
    Janati Idrissi, Mohammed Amine
    Fecha: 
    09/2016
    Palabra clave: 
    genetic algorithms; optimization; architecture; nonlinear operation; multilayer perceptron; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/11569
    DOI: 
    http://doi.org/10.9781/ijimai.2016.415
    Dirección web: 
    https://ijimai.org/journal/bibcite/reference/2523
    Open Access
    Resumen:
    The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.
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