A GA-based flexible learning algorithm with error tolerance for digital binary neural networks

Authors:
Shutaro Kabeya;Tohru Abe;Toshimichi Saito
Affiliations:
Department of Electrical and Electronics Engineering, Hosei University, Kogenai-city, Tokyo, Japan;Department of Electrical and Electronics Engineering, Hosei University, Kogenai-city, Tokyo, Japan;Department of Electrical and Electronics Engineering, Hosei University, Kogenai-city, Tokyo, Japan
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper presents a learning algorithm of digital binary neural networks for approximation of desired Boolean functions. In the learning, the genetic algorithms is used with flexible fitness that tolerates error: it is suitable to reduce the number of hidden neurons and to tolerate noise and outliers. We then apply the algorithm to design of cellular automata with rich spatio-temporal patterns and various applications. Performing basic numerical experiment, the algorithm efficiency is confirmed.