University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Exponential stability of stochastic, retarded neural networks

Joy, MP (2007) Exponential stability of stochastic, retarded neural networks ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks. pp. 67-72.

Full text not available from this repository.

Abstract

The stability analysis of neural networks is important in the applications and has been studied by many authors. However, only recently has the stability of stochastic models of neural networks been investigated. In this paper we analyse the global asymptotic stability of a class of neural networks described by a stochastic delay differential equation. It can be argued that such a model is as comprehensive as one would like to be when studying perturbations of neural networks since delay siganalling and noise are accounted for. We present a convergence theorem and discuss some examples of its use.

Item Type: Article
Authors :
NameEmailORCID
Joy, MPm.joy@surrey.ac.ukUNSPECIFIED
Date : 1 December 2007
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 10:29
Last Modified : 17 May 2017 14:49
URI: http://epubs.surrey.ac.uk/id/eprint/828002

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800