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Stability Criteria for Stochastic Recurrent Neural Networks with Two Time-Varying Delays and Impulses

by R.RAJA, S.Marshal Anthoni
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 28
Year of Publication: 2010
Authors: R.RAJA, S.Marshal Anthoni
10.5120/514-831

R.RAJA, S.Marshal Anthoni . Stability Criteria for Stochastic Recurrent Neural Networks with Two Time-Varying Delays and Impulses. International Journal of Computer Applications. 1, 28 ( February 2010), 28-35. DOI=10.5120/514-831

@article{ 10.5120/514-831,
author = { R.RAJA, S.Marshal Anthoni },
title = { Stability Criteria for Stochastic Recurrent Neural Networks with Two Time-Varying Delays and Impulses },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 28 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number28/514-831/ },
doi = { 10.5120/514-831 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:49:20.355633+05:30
%A R.RAJA
%A S.Marshal Anthoni
%T Stability Criteria for Stochastic Recurrent Neural Networks with Two Time-Varying Delays and Impulses
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 28
%P 28-35
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper is concerned with a stability problem for a class of stochastic recurrent impulsive neural networks with both discrete and distributed time-varying delays. Based on Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of impulsive neural networks. Two numerical examples are given to illustrate the effectiveness of the stability results.

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Index Terms

Computer Science
Information Sciences

Keywords

Global asymptotic stability Linear matrix inequality Lyapunov-Krasovskii functional Time-varying delays Stochastic recurrent neural networks distributed delays impulsive

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