Finite-Time Synchronizing Control for Chaotic Neural Networks

Abstract EN

This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks.

In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances.

A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances.

Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system.

Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.