Anti-periodicity on inertial Cohen-Grossberg neural networks involving distributed delays

This work investigates a class of inertial Cohen-Grossberg neural networks incorporating distributed delays using a direct mathematical analysis approach. First, by exploiting Lyapunov functional method, one can verify that the solutions of the addressed system are exponentially attractive to each other. Second, with the help of differential inequality techniques, the existence and global exponential stability of the anti-periodic solution are gained. Lastly, a simulation example is provided to illustrate the theoretical findings.