New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulate...

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Main Authors: Zixin Liu, Shu Lv, Shouming Zhong, Mao Ye
Format: Article
Language:English
Published: Hindawi Limited 2009-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2009/874582
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spelling doaj-4eb8e2217b7149f387dc92137bb62e122020-11-24T22:59:05ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2009-01-01200910.1155/2009/874582874582New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying DelayZixin Liu0Shu Lv1Shouming Zhong2Mao Ye3School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaThe robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.http://dx.doi.org/10.1155/2009/874582
collection DOAJ
language English
format Article
sources DOAJ
author Zixin Liu
Shu Lv
Shouming Zhong
Mao Ye
spellingShingle Zixin Liu
Shu Lv
Shouming Zhong
Mao Ye
New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
Discrete Dynamics in Nature and Society
author_facet Zixin Liu
Shu Lv
Shouming Zhong
Mao Ye
author_sort Zixin Liu
title New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
title_short New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
title_full New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
title_fullStr New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
title_full_unstemmed New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay
title_sort new improved exponential stability criteria for discrete-time neural networks with time-varying delay
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2009-01-01
description The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.
url http://dx.doi.org/10.1155/2009/874582
work_keys_str_mv AT zixinliu newimprovedexponentialstabilitycriteriafordiscretetimeneuralnetworkswithtimevaryingdelay
AT shulv newimprovedexponentialstabilitycriteriafordiscretetimeneuralnetworkswithtimevaryingdelay
AT shoumingzhong newimprovedexponentialstabilitycriteriafordiscretetimeneuralnetworkswithtimevaryingdelay
AT maoye newimprovedexponentialstabilitycriteriafordiscretetimeneuralnetworkswithtimevaryingdelay
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