Title :
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
fDate :
Nov. 30 2009-Dec. 2 2009
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;
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
DOI :
10.1109/ISDA.2009.149
