Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: An LMI approach

Authors:
Xiaodi Li;Xilin Fu
Affiliations:
School of Mathematical Sciences, Xiamen University, Xiamen, 361005, PR China;School of Mathematical Sciences, Shandong Normal University, Jinan, 250014, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2011

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Abstract

In this paper, we consider the stochastic Cohen-Grossberg-type BAM neural networks with mixed delays. By utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) approach, some sufficient LMI-based conditions are obtained to guarantee the global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays. These conditions can be easily checked via the MATLAB LMI toolbox. Moreover, the obtained results extend and improve the earlier publications. Finally, a numerical example is provided to demonstrate the low conservatism and effectiveness of the proposed LMI conditions.