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Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: an LMI approach. (English) Zbl 1217.34126
Summary: We consider stochastic Cohen-Grossberg-type BAM neural networks with mixed delays. By utilizing a 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 earlier publications. Finally, a numerical example is provided to demonstrate the low conservatism and the effectiveness of the proposed LMI conditions.
MSC:
34K50Stochastic functional-differential equations
34K20Stability theory of functional-differential equations
92B20General theory of neural networks (mathematical biology)
Software:
Matlab
References:
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