Reproducing chaos by variable structure recurrent neural networks

被引：7

作者：

Felix, RA ^{[1
]}

Sanchez, EN

Chen, GR

机构：

[1] CINVESTAV, Unidad Guadalajara, Guadalajara, Jalisco, Mexico

[2] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2004年 / 15卷 / 06期

关键词：

chaos generation; identification; recurrent neural networks; variable structure system;

D O I：

10.1109/TNN.2004.836236

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1450 / 1457

页数：8