STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS

被引：0

作者：

Ali, M. Syed ^{[1
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore, Tamil Nadu, India

来源：

ACTA MATHEMATICA SCIENTIA | 2015年 / 35卷 / 05期

关键词：

Lyapunov functional; linear matrix inequality; Markovian jumping parameters; recurrent neural networks; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; ASYMPTOTIC STABILITY; DEPENDENT STABILITY; NEUTRAL-TYPE; CRITERIA;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, global robust stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters is considered. A novel Linear matrix inequality(LMI) based stability criterion is obtained to guarantee the asymptotic stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters. The results are derived by using the Lyapunov functional technique, Lipchitz condition and S-procuture. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. Our results are also compared with results discussed in [31] and [34] to show the effectiveness and conservativeness.

引用

页码：1122 / 1136

页数：15

共 50 条

[1] STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS
M.SYED ALI
[J]. Acta Mathematica Scientia(English Series)., 2015, 35 (05) - 1136
[2] STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS
M.SYED ALI
[J]. Acta Mathematica Scientia, 2015, (05) : 1122 - 1136
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