A smoothing interval neural network
type: Item
instance of: scholarly article
title: A smoothing interval neural network
zbMATH Open document ID: 1257.68130
DOI: 10.1155/2012/456919
author: Dakun Yang
author: Wei Wu
published in: Discrete Dynamics in Nature and Society
publication date: 2013-02-21T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 6137736
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/456919
OpenAlex ID: W2106552479
Wikidata QID: Q58700570
cites work: Quantum complexity in mesoscopic systems. Proceedings of the Fourteenth Annual International Conference of the Center for Nonlinear Studies held in Los Alamos, New Mexico, May 16--20, 1994
cites work: Learning representations by back-propagating errors
cites work: Convergence analysis of online gradient method for BP neural networks
cites work: Convergence Analysis of Three Classes of Split-Complex Gradient Algorithms for Complex-Valued Recurrent Neural Networks
cites work: Q5566070
cites work: Q5566068
description: scientific article; zbMATH DE number 6137736