LMI conditions for stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays. (English) Zbl 1221.34195

Fu, Xilin; Li, Xiaodi

Commun. Nonlinear Sci. Numer. Simul. 16, No. 1, 435-454 (2011).

Summary: The global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is investigated by using Lyapunov–Krasovskii functional method and the linear matrix inequality (LMI) technique. The mixed time delays comprise both the multiple time-varying and continuously distributed delays. Some new sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed model in the stochastic sense using the powerful MATLAB LMI toolbox. The results extend and improve the earlier publications. Two numerical examples are given to illustrate the effectiveness of our results.

MSC:

34K20	Stability theory of functional-differential equations
34K50	Stochastic functional-differential equations
60G35	Signal detection and filtering (aspects of stochastic processes)
60H10	Stochastic ordinary differential equations (aspects of stochastic analysis)
82C32	Neural nets applied to problems in time-dependent statistical mechanics
92B20	Neural networks for/in biological studies, artificial life and related topics

Keywords:

impulsive stochastic Cohen–Grossberg neural networks; stability; Lyapunov– krasovskii functional; linear matrix inequality (LMI); multiple time-varying delays; continuously distributed delays

Software:

Matlab; LMI toolbox

References:

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