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Indexed by:期刊论文

Date of Publication:2012-01-01

Journal:DISCRETE DYNAMICS IN NATURE AND SOCIETY

Included Journals:SCIE、Scopus

Volume:2012

ISSN No.:1026-0226

Abstract:In many applications, it is natural to use interval data to describe various kinds of uncertainties. This paper is concerned with an interval neural network with a hidden layer. For the original interval neural network, it might cause oscillation in the learning procedure as indicated in our numerical experiments. In this paper, a smoothing interval neural network is proposed to prevent the weights oscillation during the learning procedure. Here, by smoothing we mean that, in a neighborhood of the origin, we replace the absolute values of the weights by a smooth function of the weights in the hidden layer and output layer. The convergence of a gradient algorithm for training the smoothing interval neural network is proved. Supporting numerical experiments are provided.