The existence and exponential attractivity of κ-almost periodic sequence solution of discrete time neural networks

In the present paper, several sufficient conditions are obtained for the existence and exponential attractivity of a unique κ-almost periodic sequence solution of discrete time neural network. Our results generalize the corresponding results about almost periodic sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential attractivity of κ-almost periodic sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.