Título: Treinamento de Redes Neurais Artificiais Utilizando Time Assíncrono
Autores: Saito Junior, Paulo Akira; Nascimento Júnior, Cairo Lúcio; Yoneyama, Takashi
Resumo: The training of artificial neural networks can be seen as a hard optimization problem. Any algorithm used to solve this problem will have weak and strong features. In this article we consider the use of Asynchronous Teams to train artificial neural networks. An Asynchronous Team is a general computational structure where several different algorithms run in parallel in different computers and are applied at the same time to solve the same optimization problem. During the computation, several intermediate solutions are analysed and used as new starting points for the different algorithms. We show that, when compared with the solutions obtained by each individual algorithm, the use of the Asynchronous Team (controlled “mixture” of different algorithms) leads to a better solution, that is, better trained artificial neural networks. As a simple example, an artificial neural network is trained to recognize a few simple characters.
Artigo em pdf: 4cbrn_020.pdf
Arquivo BibTex: 4cbrn_020.bib