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LMI conditions for stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays. (English) Zbl 1221.34195
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:
34K20Stability theory of functional-differential equations
34K50Stochastic functional-differential equations
60G35Signal detection and filtering (stochastic processes)
60H10Stochastic ordinary differential equations
82C32Neural nets (statistical mechanics)
92B20General theory of neural networks (mathematical biology)
Software:
Matlab
References:
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