Global exponential stability and existence of anti-periodic solutions to impulsive Cohen-Grossberg neural networks on time scales

Abstract

By using the method of coincidence degree theory and Lyapunov functions, some new criteria are established for the existence and global exponential stability of anti-periodic solutions to impulsive Cohen-Grossberg neural networks on time scales. Our results are new even if the time scale T = R or Z. Finally, an example is given to illustrate our results.

Anti-periodic Boundary Value Problems of <i>φ</i>-Laplacian Impulsive Differential Equations

“…Anti-periodic problem has been extensively studied during the past decades, such as anti-periodic trigonometric polynomials ( [15]). Moreover, anti-periodic boundary conditions also appear in difference and differential equations (see [16,17] and references therein).…”