Invariant Set and Periodicity for Delayed Neural Networks on Time Scales

Abstract

In this paper, we investigate invariant set and periodicity of non-autonomous neural networks with time-varying delays on time scales. Based calculus of time scales, we apply M-matrix and inequality analysis techniques to construct invariant set and obtain existence of a globally exponential periodic solution in the invariant set. Our results are general and can include continuous or discrete ones.