New Stochastic Robust Stability Criteria for Time-Varying Delay Neural Networks with Markovian Jump Parameters

被引：1

作者：

Qiu Jiqing ^{[1
]}

Shi Peng ^{[2
]}

Yang Hongjiu ^{[1
]}

Li Li ^{[1
]}

Li Jie ^{[1
]}

机构：

[1] Hebei Univ Sci & Technol, Coll Sci, Shijiazhuang 050018, Hebei, Peoples R China

[2] Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales

来源：

PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 6 | 2008年

关键词：

Markovian Jump Parameters; Interval Time-varying Delays; Linear Factional Uncertainties; Neural Networks; Robust Stability; Linear Matrix Inequalities;

D O I：

10.1109/CHICC.2008.4605130

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the problem of stochastic robust stability of interval time-varying del ay 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.

引用

页码：634 / +

页数：2

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