Anti-periodic Solutions Dynamics for Fractional-order Inertia Cohen-Grossberg Neural Networks

The dynamic behavior of anti-periodic solutions for fractional-order inertia Cohen-Grossberg neural networks is investigated in the article. First, the fractional derivative with different orders is transformed to that with the same order by properly variable substitution; Second, a sufficient condition can ensure the solution is global Mittag-Leffler stability by using properties of fractional calculus and characteristics of Mittag-Leffler function; Moreover, a sufficient condition for the existence of an anti-periodic solution is given by constructing a system sequence solution that converges to a continuous function using Arzela-Asolitheorem. In the final, we verify the correctness of the conclusion by numerical simulation.