Anti-periodic solutions of Cohen-Grossberg shunting inhibitory cellular neural networks on time scales

Abstract

In this paper, Cohen-Grossberg shunting inhibitory cellular neural networks(CGSICNNs) on time scales are investigated. Some sufficient conditions which ensure the existence and global exponential stability of anti-periodic solutions for a class of CGSICNNs on time scales are established. Numerical simulations are carried out to illustrate the theoretical findings. The results obtained in this paper are of great significance in designs and applications of globally stable anti-periodic Cohen-Grossberg shunting inhibitory cellular neural networks.

Dynamics of Shunting Inhibitory Cellular Neural Networks with Variable Two-Component Passive Decay Rates and Poisson Stable Inputs

“…It is historical fact that Poisson stability emerged in works [12,13] and then spread to many areas of applied mathematics, similarly to how periodic, quasi-periodic and almost periodic motions [14][15][16][17][18][19] spread. In recent decades, the dynamics were intensively considered in neuroscience [3,[6][7][8][20][21][22][23][24]. Currently, Poisson stable motions are the most sophisticated type of recurrence [25][26][27][28].…”