New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks

In this paper, we solve the mean-square exponential input-to-state stability problem for a class of stochastic delayed recurrent neural networks with time-varying coefficients. With the aid of stochastic analysis theory and a Lyapunov-Krasovskii functional, we derive a novel criterion that ensures t...

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Main Authors:	Wentao Wang, Shuhua Gong, Wei Chen
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2018-01-01
Series:	Systems Science & Control Engineering
Subjects:	Stochastic delayed recurrent neural networks input-to-state stability Itô's formula Lyapunov-Krasovskii functional
Online Access:	http://dx.doi.org/10.1080/21642583.2018.1544512

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spelling	doaj-10aaf0d26e924d578810d3877fed6f412020-11-25T01:35:48ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832018-01-016150150910.1080/21642583.2018.15445121544512New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networksWentao Wang0Shuhua Gong1Wei Chen2Shanghai University of Engineering ScienceJiaxing UniversityShanghai Lixin University of Accounting and FinanceIn this paper, we solve the mean-square exponential input-to-state stability problem for a class of stochastic delayed recurrent neural networks with time-varying coefficients. With the aid of stochastic analysis theory and a Lyapunov-Krasovskii functional, we derive a novel criterion that ensures the given system is mean-square exponentially input-to-state stable. Furthermore, the new criterion generalizes and improves some known results. Finally, two examples and their numerical simulations are provided to demonstrate the theoretical results.http://dx.doi.org/10.1080/21642583.2018.1544512Stochastic delayed recurrent neural networksinput-to-state stabilityItô's formulaLyapunov-Krasovskii functional
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Wentao Wang Shuhua Gong Wei Chen
spellingShingle	Wentao Wang Shuhua Gong Wei Chen New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks Systems Science & Control Engineering Stochastic delayed recurrent neural networks input-to-state stability Itô's formula Lyapunov-Krasovskii functional
author_facet	Wentao Wang Shuhua Gong Wei Chen
author_sort	Wentao Wang
title	New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
title_short	New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
title_full	New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
title_fullStr	New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
title_full_unstemmed	New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
title_sort	new result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks
publisher	Taylor & Francis Group
series	Systems Science & Control Engineering
issn	2164-2583
publishDate	2018-01-01
description	In this paper, we solve the mean-square exponential input-to-state stability problem for a class of stochastic delayed recurrent neural networks with time-varying coefficients. With the aid of stochastic analysis theory and a Lyapunov-Krasovskii functional, we derive a novel criterion that ensures the given system is mean-square exponentially input-to-state stable. Furthermore, the new criterion generalizes and improves some known results. Finally, two examples and their numerical simulations are provided to demonstrate the theoretical results.
topic	Stochastic delayed recurrent neural networks input-to-state stability Itô's formula Lyapunov-Krasovskii functional
url	http://dx.doi.org/10.1080/21642583.2018.1544512
work_keys_str_mv	AT wentaowang newresultonthemeansquareexponentialinputtostatestabilityofstochasticdelayedrecurrentneuralnetworks AT shuhuagong newresultonthemeansquareexponentialinputtostatestabilityofstochasticdelayedrecurrentneuralnetworks AT weichen newresultonthemeansquareexponentialinputtostatestabilityofstochasticdelayedrecurrentneuralnetworks
_version_	1725066218447044608

New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks

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