Almost sure exponential stability on interval stochastic neural networks with time-varying delays

Authors:
Wudai Liao;Zhongsheng Wang;Xiaoxin Liao
Affiliations:
School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, P.R. China;School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, P.R. China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Because of VLSI realization of artificial neural networks and measuring the elements of the circuits, noises coming from the circuits and the errors of the parameters of the network systems are therefore unavoidable. Making use of the stochastic version of Razumikhin theorem of stochastic functional differential equation, Lyapunov direct methods and matrix analysis,almost sure exponential stability on interval neural networks perturbed by white noises with time varying delays is examined, and some sufficient algebraic criteria which only depend on the systems’ parameters are given. For well designed deterministic neural networks, the results obtained in the paper also imply that how much tolerance against perturbation they have.