New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks

The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay is investigated. The delay-dependent sufficient criteria are derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some pr...

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Main Author: Jianjiang Yu
Format: Article
Language:English
Published: Wiley 2009-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2009/139671
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author Jianjiang Yu
author_facet Jianjiang Yu
author_sort Jianjiang Yu
collection DOAJ
description The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay is investigated. The delay-dependent sufficient criteria are derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some previous results and are less conservative than the existing works. Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved. Two numerical examples are given to show the effectiveness and the benefits of the proposed method.
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spelling doaj-art-8c46aeb7019d4e0c962e56976eecfb242025-08-20T02:09:13ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2009-01-01200910.1155/2009/139671139671New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural NetworksJianjiang Yu0School of Information Science and Technology, Yancheng Teachers University, Yancheng 224002, ChinaThe problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay is investigated. The delay-dependent sufficient criteria are derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some previous results and are less conservative than the existing works. Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved. Two numerical examples are given to show the effectiveness and the benefits of the proposed method.http://dx.doi.org/10.1155/2009/139671
spellingShingle Jianjiang Yu
New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
Discrete Dynamics in Nature and Society
title New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
title_full New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
title_fullStr New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
title_full_unstemmed New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
title_short New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks
title_sort new results on passivity analysis of delayed discrete time stochastic neural networks
url http://dx.doi.org/10.1155/2009/139671
work_keys_str_mv AT jianjiangyu newresultsonpassivityanalysisofdelayeddiscretetimestochasticneuralnetworks