Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays

doi:10.1088/1674-1056/23/6/060702

中国物理B ›› 2014, Vol. 23 ›› Issue (6): 60702-060702.doi: 10.1088/1674-1056/23/6/060702

Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays

M. Syed Ali

Department of Mathematics, Thiruvalluvar University, Vellore-632 115, Tamilnadu, India

收稿日期:2013-11-12 修回日期:2013-12-12 出版日期:2014-06-15 发布日期:2014-06-15
基金资助:
Project supported by DST Project (Grant No. SR/FTP/MS-039/2011).

Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays

M. Syed Ali

Department of Mathematics, Thiruvalluvar University, Vellore-632 115, Tamilnadu, India

Received:2013-11-12 Revised:2013-12-12 Online:2014-06-15 Published:2014-06-15
Contact: M. Syed Ali E-mail:syedgru@gmail.com
Supported by:
Project supported by DST Project (Grant No. SR/FTP/MS-039/2011).

摘要/Abstract

摘要： In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen-Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples.

关键词: Cohen-Grossberg neural networks, global asymptotic stability, linear matrix inequality, Lyapunov functional, time-varying delays

Abstract: In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen-Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples.

Key words: Cohen-Grossberg neural networks, global asymptotic stability, linear matrix inequality, Lyapunov functional, time-varying delays

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

05.45.Gg (Control of chaos, applications of chaos) 02.30.Ks (Delay and functional equations) 02.30.Sa (Functional analysis)

M. Syed Ali. Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays[J]. 中国物理B, 2014, 23(6): 60702-060702.

M. Syed Ali. Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays[J]. Chin. Phys. B, 2014, 23(6): 60702-060702.

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Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays

Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays

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