Almost sure stability of stochastic neutral Cohen-Grossberg neural networks with Levy noise and time-varying delays

摘要

The almost sure stability for the stochastic neutral Cohen-Grossberg neural networks (SNCGNNs) with Levy noise, time-varying delays, and Markovian switching would be deliberated in this article. By means of the nonnegative semimartingale convergence theorem (NSCT), the neutral Ito formula, M-matrix method, and selecting appropriate Lyapunov function, several almost sure stability criterions for the SNCGNNs could be derived. Moreover, according to the M-matrix theory, the upper bounds of the coefficients at any mode are given. Finally, two examples and numerical simulations verify the correctness of theoretical analysis for the stability criterions proposed in the article.

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