A Smoothing Interval Neural Network | Zendy

Dakun Yang | Zendy; Wei Wu | Zendy

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A Smoothing Interval Neural Network

Author(s) -

Dakun Yang,

Wei Wu

Publication year - 2012

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2012/456919

Subject(s) - smoothing , interval (graph theory) , artificial neural network , convergence (economics) , computer science , oscillation (cell signaling) , algorithm , function (biology) , artificial intelligence , mathematics , combinatorics , evolutionary biology , biology , economics , computer vision , genetics , economic growth

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

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