Mean-square stability of delayed stochastic neural networks with impulsive effects driven by G-Brownian motion

This paper studies the mean-square exponential input-to-state stability for a class of delayed impulsive stochastic Cohen-Grossberg neural networks driven by G-Brownian motion. By constructing an appropriate G-Lyapunov-Krasovskii functional, mathematical induction approach and some inequality techniques, a new set of sufficient conditions is obtained for the mean-square exponential input-to-state stability of the trivial solutions for the considered systems. Finally, an example is given to illustrate the obtained theory.