Global exponential stability of impulsive discrete-time neural networks with time-varying delays

This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type in...

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Main Authors: Xu, Honglei, Chen, Y., Teo, Kok Lay
Format: Journal Article
Published: Elsevier 2010
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/16829
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author Xu, Honglei
Chen, Y.
Teo, Kok Lay
author_facet Xu, Honglei
Chen, Y.
Teo, Kok Lay
author_sort Xu, Honglei
building Curtin Institutional Repository
collection Online Access
description This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type inequality technique, sufficient conditions for ensuring global exponential stability of discrete-time neural networks are derived, and the estimated exponential convergence rate is provided as well. The obtained results are then applied to derive global exponential stability criteria and exponential convergence rate of impulsive discrete-time neural networks with time-varying delays. Finally, numerical examples are provided to illustrate the effectiveness and usefulness of the obtained criteria.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:18:34Z
publishDate 2010
publisher Elsevier
recordtype eprints
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spelling curtin-20.500.11937-168292019-02-19T04:27:47Z Global exponential stability of impulsive discrete-time neural networks with time-varying delays Xu, Honglei Chen, Y. Teo, Kok Lay Exponential convergence rate Global exponential stability Halanay inequality Impulsive discrete-time neural networks This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type inequality technique, sufficient conditions for ensuring global exponential stability of discrete-time neural networks are derived, and the estimated exponential convergence rate is provided as well. The obtained results are then applied to derive global exponential stability criteria and exponential convergence rate of impulsive discrete-time neural networks with time-varying delays. Finally, numerical examples are provided to illustrate the effectiveness and usefulness of the obtained criteria. 2010 Journal Article http://hdl.handle.net/20.500.11937/16829 10.1016/j.amc.2010.05.087 Elsevier fulltext
spellingShingle Exponential convergence rate
Global exponential stability
Halanay inequality
Impulsive discrete-time neural networks
Xu, Honglei
Chen, Y.
Teo, Kok Lay
Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title_full Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title_fullStr Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title_full_unstemmed Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title_short Global exponential stability of impulsive discrete-time neural networks with time-varying delays
title_sort global exponential stability of impulsive discrete-time neural networks with time-varying delays
topic Exponential convergence rate
Global exponential stability
Halanay inequality
Impulsive discrete-time neural networks
url http://hdl.handle.net/20.500.11937/16829