Dynamic system identification via recurrent multilayer perceptrons

被引：36

作者：

Li, XO

Yu, W

机构：

[1] CINVESTAV, IPN, Dept Automat Control, Mexico City 07360, DF, Mexico

[2] CINVESTAV, IPN, Dept Ingn Elect, Secc Computac, Mexico City 07360, DF, Mexico

来源：

INFORMATION SCIENCES | 2002年 / 147卷 / 1-4期

关键词：

system identification; function approximation; recurrent multilayer perceptrons;

D O I：

10.1016/S0020-0255(02)00207-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper continuous-time recurrent multilayer perceptrons (RMLP) are proposed to identify nonlinear systems. Using the function approximation theorem for multilayer perceptrons (MLP), we conclude that RMLP can approximate any dynamic system in any degree of accuracy. By means of a Lyapunov-like analysis, a stable learning algorithm for RMLP is determined. The suggested learning algorithm is similar to the well-known backpropagation rule of the MLP but with an additional term which assure the stability of identification error. (C) 2002 Elsevier Science Inc. All rights reserved.

引用

页码：45 / 63

页数：19

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