Dynamic structure-based neural network determination using orthogonal genetic algorithm with quantization

Authors:
Liling Xing;Wentao Zhao
Affiliations:
College of Information System and Management, National University of Defense Technology, Changsha, China;Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

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Abstract

It proposed a novel dynamic structure-based neural network determination approach using orthogonal genetic algorithm with quantization in this paper. Both the parameter (the threshold of each neuron and the weight between two neurons) and the transfer function (the transfer function of each layer and the network training function) of the dynamic structure-based neural network were optimized in this proposed approach. In order to satisfy the dynamic transform of the neural network structure, the population adjustment operation was introduced into orthogonal genetic algorithm with quantization for dynamic modification of the population's dimensionality. A mathematical example was applied to evaluate this proposed approach. The experiment results suggested that this proposed approach is feasible, correct and valid.