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A Smoothing Interval Neural Network

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  • Dakun Yang
  • Wei Wu

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.

Suggested Citation

  • Dakun Yang & Wei Wu, 2012. "A Smoothing Interval Neural Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-25, December.
    • Handle: RePEc:hin:jnddns:456919
    DOI: 10.1155/2012/456919
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