Dynamic behavior analysis of Stepanov-like almost periodic solution in distribution sense for stochastic neural network with delays

Abstract

In this paper, a class of stochastic neural networks with time delays is studied. Based on the contraction mapping principle, sufficient conditions are derived to ensure the existence of Stepanov-like almost periodic solutions for the stochastic neural networks under consideration. Then, by designing a novel state-feedback controller and constructing a suitable Lyapunov function, the global asymptotic synchronization and exponential stability of the stochastic neural networks are researched. Finally, two numerical examples are provided to show the feasibility of our results.