Stability of stochastic Markovian jump neural networks with mode-dependent delays

被引：39
|
作者：
Ma, Qian [1 ]
Xu, Shengyuan [1 ]
Zou, Yun [1 ]
Lu, Jinjun [2 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China
[2] Nantong Univ, Sch Elect Engn, Nan tong 229019, Peoples R China
来源：
NEUROCOMPUTING | 2011年 / 74卷 / 12-13期
关键词：
Asymptotic stability; Stochastic neural networks; Markovian jumping parameters; Mixed mode-dependent delays; MIXED TIME DELAYS; ROBUST EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; VARYING DELAYS; PARAMETERS; SYSTEMS; CRITERIA;
D O I：
10.1016/j.neucom.2011.01.016
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the problem of stability analysis for a general class of uncertain stochastic neural networks with Markovian jumping parameters and mixed mode-dependent delays is considered. By the use of a new Markovian switching Lyapunov-Krasovskii functional, delay-dependent conditions on mean square asymptotic stability are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the proposed approach. (C) 2011 Published by Elsevier B.V.
引用
收藏
页码：2157 / 2163
页数：7
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