Letters: Robust stability of stochastic Cohen-Grossberg neural networks with mixed time-varying delays

Authors:
Tao Li;Ai-guo Song;Shu-min Fei
Affiliations:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2009

Citing 9
Cited 7

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Abstract

In the paper, the problem of robust exponential stability analysis is investigated for stochastic Cohen-Grossberg neural networks with both interval time-varying and distributed time-varying delays. By employing an augmented Lyapunov-Krasovskii functional, together with the LMI approach and definition on convex set, two delay-dependent conditions guaranteeing the robust exponential stability (in the mean square sense) of addressed system are presented. Additionally, the activation functions are of more general descriptions and the derivative of time-varying delay being less than 1 is released, which generalize and further improve those earlier methods. Numerical examples are provided to demonstrate the effectiveness of proposed stability conditions.