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Chin. Phys. B, 2014, Vol. 23(6): 060702    DOI: 10.1088/1674-1056/23/6/060702
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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
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.
Keywords:  Cohen-Grossberg neural networks      global asymptotic stability      linear matrix inequality      Lyapunov functional      time-varying delays  
Received:  12 November 2013      Revised:  12 December 2013      Accepted manuscript online: 
PACS:  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
  05.45.Gg (Control of chaos, applications of chaos)  
  02.30.Ks (Delay and functional equations)  
  02.30.Sa (Functional analysis)  
Fund: Project supported by DST Project (Grant No. SR/FTP/MS-039/2011).
Corresponding Authors:  M. Syed Ali     E-mail:  syedgru@gmail.com

Cite this article: 

M. Syed Ali Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays 2014 Chin. Phys. B 23 060702

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