Global Robust Passivity Analysis for Stochastic Interval Neural Networks with Interval Time-Varying Delays and Markovian Jumping Parameters

被引：14
|
作者：
Balasubramaniam, P. [1 ]
Nagamani, G. [1 ]
机构：
[1] Deemed Univ, Gandhigram Rural Inst, Dept Math, Gandhigram 624302, Tamil Nadu, India
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2011年 / 149卷 / 01期
关键词：
Interval time-varying delays; Linear matrix inequality (LMI); Lyapunov method; Passivity; Stochastic interval neural networks; NONLINEAR-SYSTEMS; STABILITY; PASSIFICATION; DISCRETE;
D O I：
10.1007/s10957-010-9770-6
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
In this paper, the problem of passivity analysis is investigated for stochastic interval neural networks with interval time-varying delays and Markovian jumping parameters. By constructing a proper Lyapunov-Krasovskii functional, utilizing the free-weighting matrix method and some stochastic analysis techniques, we deduce new delay-dependent sufficient conditions, that ensure the passivity of the proposed model. These sufficient conditions are computationally efficient and they can be solved numerically by linear matrix inequality (LMI) Toolbox in Matlab. Finally, numerical examples are given to verify the effectiveness and the applicability of the proposed results.
引用
收藏
页码：197 / 215
页数：19
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