New Criteria on Delay-Dependent Robust Stability for Uncertain Markovian Stochastic Delayed Neural Networks

被引：2
|
作者：
Chen, Huabin [1 ]
Wang, Jinjing [1 ]
Wang, Liu [1 ]
机构：
[1] Nanchang Univ, Sch Sci, Nanchang 330031, Jiangxi, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2015年 / 42卷 / 02期
关键词：
Uncertain stochastic neural networks; Time-varying delay; Lyapunov-rasovskii functional; Linear matrix inequalities (LMIs); Exponential stability; Markovian Jump parameters; SQUARE EXPONENTIAL STABILITY; MIXED TIME-DELAYS; MEAN-SQUARE; JUMP PARAMETERS; DISCRETE;
D O I：
10.1007/s11063-014-9356-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is mainly concerned about the problem of the robustly exponential stability of uncertain stochastic neural networks with time-varying delay and Markovian jump parameters. Some new delay-dependent stability criteria are established for the considered systems by constructing a modified Lyapunov-rasovskii functional, which are expressed in terms of linear matrix inequalities. Compared with some existing ones, our obtained results have a potential less conservatism. Finally, three illustrative numerical examples are provided to show the effectiveness of the obtained results.
引用
收藏
页码：275 / 290
页数：16
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