## Dynamic analysis of Markovian jumping impulsive stochastic Cohen-Grossberg neural networks with discrete interval and distributed time-varying delays.(English)Zbl 1194.93191

Summary: The dynamic analysis problem is considered for a new class of Markovian jumping impulsive stochastic Cohen-Grossberg Neural Networks (CGNNs) with discrete interval and distributed delays. The parameter uncertainties are assumed to be norm bounded and the discrete delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the Lyapunov-Krasovskii functional and stochastic stability theory, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities. Some asymptotic stability criteria are formulated by means of the feasibility of a Linear Matrix Inequality (LMI), which can be easily calculated by LMI Toolbox in MATLAB. A numerical example is provided to show that the proposed results significantly improve the allowable upper bounds of delays known from the literature.

### MSC:

 93D20 Asymptotic stability in control theory 60J75 Jump processes (MSC2010) 92B20 Neural networks for/in biological studies, artificial life and related topics 93E03 Stochastic systems in control theory (general) 15A39 Linear inequalities of matrices

### Software:

LMI toolbox; Matlab
Full Text:

### References:

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