Existence and attractivity of periodic solutions to non-autonomous Cohen-Grossberg neural networks with time delays

In this paper, we investigate the existence and attractivity of periodic solutions to non-autonomous Cohen-Grossberg neural networks with connection time delays for both discrete and distributed cases. By combining the Lyapunov functional method with the contraction mapping principle, we establish several criteria for the existence and global exponential stability of periodic solutions. More interestingly, all the criteria are independent of time delays as well as the delay types, and do not include one another. Several examples with numerical simulations are given to demonstrate the results.