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

Abstract 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.

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