almost periodic attractors for Cohen–Grossberg-type BAM neural networks with variable coefficients and distributed delays

In this paper, a class of 2 N almost periodic attractors for Cohen–Grossberg-type bi-directional associative memory (BAM) neural networks with variable coefficients and distributed delays is discussed. By imposing some new assumptions on activation functions and system parameters, we split invariant basin of BAM into 2 N compact convex subsets. Then the existence of 2 N almost periodic solutions lying in compact convex subsets is attained. And some new criteria for the networks to converge toward these 2 N almost periodic solutions and exponential attracting domains are also given correspondingly. Finally, some examples are presented to illustrate the feasibility and effectiveness of the results.