Stabilization of delayed chaotic neural networks by periodically intermittent control

Abstract

This paper studies the exponential stabilization of delayed chaotic neural networks (DCNNs) using what is called periodically intermittent control. An exponential stability criterion for the controlled neural networks, together with its simplified version, is established by using the Lyapunov function and Halanay inequality. The feasible region of control parameters is estimated in a rigorous way. Theoretical results and numerical simulations show that the continuous-time DCNN can be stabilized by intermittent feedback control with nonzero duration. © Birkhäuser Boston 2009.