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Mathematical Problems in Engineering
Volume 2014, Article ID 162610, 7 pages
http://dx.doi.org/10.1155/2014/162610
Research Article

Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

Facultad de Ciencias Fisico Matematicas, Universidad Autonoma de Nuevo Leon (UANL), 66451 San Nicolás de los Garza, NL, Mexico

Received 21 March 2013; Accepted 12 September 2013; Published 2 January 2014

Academic Editor: Chuandong Li

Copyright © 2014 Jose P. Perez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.