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Global robust stability criteria of stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays. (English) Zbl 1221.37196
Summary: The paper is concerned with the problem of robust asymptotic stability analysis of stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technology, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed conditions can be checked easily with the LMI Control Toolbox in Matlab. Furthermore, all the results are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. A numerical example is given to demonstrate the effectiveness of our results.
MSC:
37N25Dynamical systems in biology
34K20Stability theory of functional-differential equations
34F05ODE with randomness
60H10Stochastic ordinary differential equations
62M45Neural nets and related approaches (inference from stochastic processes)
92B20General theory of neural networks (mathematical biology)
Software:
Matlab
References:
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