Delay-dependent Stability for Uncertain Stochastic Neural Networks with Distributed Delays

被引：0

作者：

Gao, Ming ^{[1
]}

Sheng, Li ^{[2
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao 266590, Peoples R China

[2] East China Univ Petr, Coll Informat & Control Engn, Qingdao 266580, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

基金：

高等学校博士学科点专项科研基金;

关键词：

Delay-dependent Criteria; Robust Stability; Recurrent Neural Networks; Stochastic Systems; Distributed Delays; TIME-VARYING DELAYS; EXPONENTIAL STABILITY; ROBUST STABILITY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with the problem of delay dependent robust stability for a class of uncertain stochastic recurrent neural networks (USRNNs) with discrete and distributed delays. In such systems, both parameter uncertainties and stochastic perturbations are taken into account. The parameter uncertainties are norm-bounded and the stochastic perturbations are in the form of a Brownian motion. Based on the Lyapunov stability theory and the linear matrix inequality (LMI) technique, some delay-dependent stability criteria are derived, which guarantee the global robust asymptotic stability in the mean square for the USRNNs. Two simulation examples are provided to illustrate the effectiveness of the proposed criteria.

引用

页码：1495 / 1500

页数：6

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