Reproducing chaos by variable structure recurrent neural networks

Author

Felix, Ramon A. ; Sanchez, Edgar N. ; Chen, Guanrong

Author_Institution

CINVESTAV, Guadalajara, Spain

Volume

15

Issue

6

fYear

2004

Firstpage

1450

Lastpage

1457

Abstract

In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed.

Keywords

chaos; computational complexity; identification; neurocontrollers; nonlinear control systems; recurrent neural nets; variable structure systems; chaos reproduction; chaotic system; computational complexity; identification error; neural network identifier; variable structure recurrent neural network; Chaos; Computational complexity; Control system synthesis; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Orbits; Recurrent neural networks; Switched systems; Chaos generation; identification; recurrent neural networks; variable structure system; Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Feedback; Logistic Models; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2004.836236

Filename

1353281