Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Networks

Abstract

In this paper the problem of trajectory tracking is studied. Based on the Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a fractional recurrent neural network and the state of each single node of a fractional complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a simple network with four different nodes and five non-uniform links.