Resumo

MEJIAS CESAR, Yuleidys; CARRASCO VELAR, Ramón; OCHOA IZQUIERDO, Isbel  e  MORENO LEMUS, Edel. Transfer functions in the multilayer perceptron: effects of its combination on local and distributed training. RCIM [online]. 2013, vol.5, n.2, pp. 186-199. ISSN 1684-1859.

The multilayer perceptron (PMC) ranks among the types of artificial neural networks (ANN), which has provided better results in studies of structure-activity relationship. As the data volumes in Bioinformatics' projects are eventually big, it was proposed to evaluate algorithms to shorten the training time of the network without affecting its efficiency. There were evaluated different tools that work with ANN and were selected Weka algorithm for extracting the network and the Platform for Distributed Task Tarenal to distribute the training of multilayer perceptron. Finally, it was developed a training algorithm for local and distributed the MLP with the possibility of varying transfer functions. It was shown that depending on the training sample, the change of transfer functions can yield results much more efficient than those obtained with the classic sigmoid function with increased g-media between 4.5 and 17 %. Moreover, it was found that with distributed training can be achieved eventually, better results than those achieved in the local environment.

Palavras-chave : transfer functions; multi-layer perceptron; neural networks.

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