Recurrent Neural Networks Design by Means of Multi-Objective Genetic Algorithm Case study : Phoneme Recognition
نویسندگان
چکیده
Evolutionary algorithms are considered more efficient for optimal system design because they can provide higher opportunity for obtaining the global optimal solution. This paper introduces a method for construct and train Recurrent Neural Networks (RNN) by means of Multi-Objective Genetic Algorithms (MOGA). The use of a multi-objective evolutionary algorithm allows the definition of many objectives in a natural way. The case study of the proposed model is the phoneme recognition. We have shown that the proposed model is able to achieve good results in recognition tasks.
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