Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations

Abstract

In this brief, we design a periodically intermittent controller to stabilize a class of networks by using discrete-time observations on the states of white noise, which will cut costs by decreasing observation frequency and controlled time. The supremum of discrete-time observations is derived by a transcendental equation. Sufficient conditions are obtained to exponentially stabilize the underlying networks. A numerical example is provided to illustrate the effectiveness and advantages of the proposed new design technique.