Discrete-time recurrent high order neural networks for nonlinear identification

被引：41

作者：

Alanis, Alma Y. ^{[1
]}

Sanchez, Edgar N. ^{[2
]}

Loukianov, Alexander G. ^{[2
]}

Hernandez, EstebanA. ^{[3
]}

机构：

[1] Univ Guadalajara, CUCEI, Zapopan 45080, Jalisco, Mexico

[2] CINVESTAV, Unidad Guadalajara, Guadalajara 45091, Jalisco, Mexico

[3] NUI Maynooth, Hamilton Inst, Maynooth, Kildare, Ireland

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2010年 / 347卷 / 07期

关键词：

Recurrent neural networks - Nonlinear systems;

D O I：

10.1016/j.jfranklin.2010.05.018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on the problem of discrete-time nonlinear system identification via recurrent high order neural networks. It includes the respective stability analysis on the basis of the Lyapunov approach for the NN training algorithm. Applicability of the proposed scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1253 / 1265

页数：13

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