Almost periodic solutions of Cohen-Grossberg neural networks with time-varying delay and variable impulsive perturbations (Q2205382)

The authors deal with a class of Cohen-Grossberg neural networks with time-varying delay and variable impulsive perturbations, denoted CGNN. The corresponding system of differential equations with initial conditions has a solution under appropriate assumptions; the system has an almost periodic solution if an additional restriction is satisfied (see Theorem 3.1). Under the same assumptions, the almost periodic solution is \textit{perfectly exponentially stable} (see Theorem 3.4). The authors also investigate a so-called uncertain CGNN with time-varying delay and variable impulsive perturbations. Under appropriate conditions, the corresponding system has an almost periodic solution which is \textit{perfectly robustly exponentially stable} (see Theorem 4.2). An example is given to illustrate the effectiveness of the results.