Control for nonlinear chaos based on radial basis function neural networks

Author

Wen, Tan ; Nan, Wang-Yao ; Wu, Zhou-Shao ; Nian, Wang-Jun

Author_Institution

Dept.of Inf. & Electr. Eng., XiangTan Polytech. Univ., China

Volume

2

fYear

2002

fDate

2002

Firstpage

1505

Abstract

A method for control of nonlinear chaotic dynamical systems based on radial basis function neural networks is presented. Combining input-output data obtained from a perturbation parameter model with a linear learning algorithm, neural networks are trained to generate the small disturbance control, then to stabilize the chaotic system. The unstable periodic orbit in the Henon map is directed to a stable fixed point by the method. The simulations show the proposed scheme has great effectiveness.

Keywords

chaos; learning (artificial intelligence); neurocontrollers; nonlinear control systems; nonlinear dynamical systems; radial basis function networks; Henon map; input-output data; linear learning algorithm; nonlinear chaotic dynamical system; perturbation parameter model; radial basis function neural networks; small disturbance control; unstable periodic orbit; Chaos; Control systems; Educational institutions; Electrical engineering; Neural networks; Nonlinear control systems; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on

Print_ISBN

0-7803-7268-9

Type

conf

DOI

10.1109/WCICA.2002.1020836

Filename

1020836