New learning strategy for supervised neural network: MPCA meta-heuristic approach

Título: New learning strategy for supervised neural network: MPCA meta-heuristic approach

Autores: Anochi, Juliana; Sambatti, Sabrina; Luz, Eduardo; Velho, Haroldo Campos

Resumo: The problem of parameter optimization for a feedforward artificial neural network (ANN) to determined its best architecture is addressed. A new metaheuristic called Multiple Particle Collision Algorithm (MPCA), introduced by Luz et al. [12], was applied to design an optimum architecture for two models of supervised neural network: the Multilayer Perceptron (MLP), and recurrent Elman network. The NN obtained using this approach is said to be self-configurable. In addition, two strategies are employed for calculating the connection weights to the MLP and Elman networks: MPCA, and backpropagation algorithm. The resulting ANNs were applied to predict the monthly mesoscale climate for the precipitation field. The comparison is performed between the ANN configuration obtained by automatic process and another configuration proposed by a human specialist.

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Páginas: 6

Código DOI: 10.21528/CBIC2013-154

Artigo em pdf: bricsccicbic2013_submission_154.pdf

Arquivo BibTex: bricsccicbic2013_submission_154.bib