Almost sure exponential stability of hybrid stochastic delayed Cohen–Grossberg neural networks

Abstract

Benefit from the significant work of Song and Mao (2018), this paper focuses on the almost sure exponential stability of hybrid stochastic delayed Cohen-Grossberg neural network (SDCGNN) with the nonlinear disturbance.By virtue of stationary distribution, Lyapunov stability theory and LMI tool, we will propose that if the corresponding delay-free stochastic neural network is almost surely exponentially stable, then there exists a positive number 𝜏 * such that the SDCGNN is also almost surely exponentially stable so long as 𝜏 < 𝜏 * . Finally, an example is given to verify the effectiveness of our results, and an implicit lower bound for 𝜏 * is provided.