Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/88019
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Type: Conference paper
Title: New Stochastic robust stability criteria for time-varying delay neural networks with Markovian jump parameters
Author: Qiu, J.
Shi, P.
Yang, H.
Li, L.
Li, J.
Citation: Proceedings of the 27th Chinese Control Conference 2008, (CCC 2008), 2008, pp.634-637
Publisher: IEEE
Publisher Place: China
Issue Date: 2008
ISBN: 9787900719706
Conference Name: Chinese Control Conference (CCC) (16 Jul 2008 - 18 Jul 2008 : Kunming, Yunnan, China)
Statement of
Responsibility: 
Qiu Jiqing, Shi Peng, Yang Hongjiu, Li Li, Li Jie
Abstract: In this paper, the problem of stochastic robust stability of interval time-varying delay neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. The linear factional uncertainty is considered, it means that a less conservative result will be obtained than using norm-bounded parameter uncertainties. And the derivative of the delay function can exceed one. Based on the lyapunov-krasovskii functional approach, a new delay-dependent stochastic stability criteria is presented in terms of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
DOI: 10.1109/CHICC.2008.4605130
Appears in Collections:Aurora harvest 7
Electrical and Electronic Engineering publications

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