ANTI-PERIODIC SOLUTIONS FOR NEURAL NETWORKS WITH DELAYS AND IMPULSES

In this paper we investigate a class of artificial neural networks with delays subject to periodic impulses. By exploiting Lyapunov functions, we analyze the global exponential stability of an arbitrary solution with initial value being bounded by γ. Further, we discuss the existence of anti-periodic solutions by constructing fundamental function sequences based on a solution with initial value being bounded by γ. We also establish sufficient conditions to ensure the existence, uniqueness and exponential stability of anti-periodic solutions, which are new and easily verifiable. At last, we present a network with its time-series and phase graphics to demonstrate our results.