Stability of stochastic cohen-grossberg neural networks with mixed time delay and Markovian parameters

Authors:
Junxiang Lu;ShanShan Wang;Chengyi Zhang
Affiliations:
School of Science, Xi'an Polytechnic University, Xi'an, P.R. China;School of Science, Xi'an Polytechnic University, Xi'an, P.R. China;School of Science, Xi'an Polytechnic University, Xi'an, P.R. China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Year:
2011

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Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

IEEE Transactions on Neural Networks

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Abstract

This paper mainly concerns stochastically asymptotical stability analysis problems for a class of stochastic Cohen-Grossberg neural networks with mixed time delays and Markovian parameters (SDCGNNswM). Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive the sufficient conditions guaranteeing the stochastically asymptotical stability of the equilibrium point. All the obtained results are presented in term of linear matrix inequalities. The efficiency of the proposed results is demonstrated via a numerical example.