Periodic solutions for stochastic Cohen-Grossberg neural networks with time-varying delays

This paper is concerned with the periodic solutions for a class of stochastic Cohen-Grossberg neural networks with time-varying delays. Since there is a nonlinearity in the leakage terms of stochastic Cohen-Grossberg neural networks, some techniques are needed to overcome the difficulty in dealing with the nonlinearity. By applying fixed points principle and Gronwall- Bellman inequality, some sufficient conditions on the existence and exponential stability of periodic solution for the stochastic neural networks are established. Moreover, a numerical example is presented to validate the theoretical results. Our results are also applicable to the existence and exponential stability of periodic solution for the corresponding deterministic systems.

Automated summary

This paper investigates the periodic solutions for a class of stochastic neural networks with time-varying delays, establishing sufficient conditions on the existence and exponential stability of periodic solution using fixed points principle and Gronwall-Bellman inequality. The theoretical results are validated through a numerical example, showing applicability to the corresponding deterministic systems.