Trajectory Tracking of Complex Dynamical Network for Recurrent Neural Network Via Control V-Stability

Author

Perez, J.P. ; Perez, J.M. ; Gonzalez, Jose A.

Author_Institution

Fac. de Cienc. Fisico-Mat., Univ. Autonoma de Nuevo Leon (UANL), San Nicolas de los Garza, Mexico

fYear

2009

fDate

Nov. 30 2009-Dec. 2 2009

Firstpage

1274

Lastpage

1277

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 recurrent neural network and the state of each single node of a 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.

Keywords

Lyapunov methods; asymptotic stability; neurocontrollers; position control; recurrent neural nets; Lyapunov theory; complex dynamical network; control V-stability; global asymptotic stability; recurrent neural network; trajectory tracking; Asymptotic stability; Automatic control; Control systems; Intelligent networks; Intelligent systems; Neural networks; Recurrent neural networks; Target recognition; Target tracking; Trajectory; Lyapunov analysis; Trajectory tracking; V-stability; complex dynamical network; recurrent neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Conference_Location

Pisa

Print_ISBN

978-1-4244-4735-0

Electronic_ISBN

978-0-7695-3872-3

Type

conf

DOI

10.1109/ISDA.2009.149

Filename

5364007