Impulsive Stabilization of Periodic Solutions of Recurrent Neural Networks with Discrete and Distributed Delays

This paper is concerned with impulsive stabilization of periodic solutions of recurrent neural networks (RNNs) with discrete and distributed delays. By considering two different types of bounded discrete-delays, two stability criteria are formulated respectively for the case where the information on the discrete-delay derivative is unknown and the case where the discrete-delay derivative be strictly less than one. It is shown that the first stability criterion is an essential improvement over the existing one in the literature. When the discrete-delay is constant, the second stability criterion is proved to be less conservative than the first stability criterion. Moreover, impulsive control law design for delayed RNNs is facilitated by adjustable parameters in the stability criteria. The usefulness of the theoretical findings is demonstrated by a numerical example.