Abstract
Multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice in particular the activation function type used for each neuron has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture and weights, for solving the obtained model we use the meta-heuristics and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.
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