Fast Training of Neural Networks for Nonlinear Dynamic System Identification

被引：1

作者：

Deflorian, Michael ^{[1
]}

Kloepper, Florian ^{[2
]}

机构：

[1] BMW Grp, Forsch & Innovat Zentrum, D-80788 Munich, Germany

[2] BMW AG, Forsch & Innovat Zentrum, D-80788 Munich, Germany

来源：

AT-AUTOMATISIERUNGSTECHNIK | 2011年 / 59卷 / 02期

关键词：

Recurrent neural networks; autocorrelated error; NARX; NOE; LEAST-SQUARES ALGORITHM; REGRESSION;

D O I：

10.1524/auto.2011.0898

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The time consuming training of dynamic neural networks often prohobits the industrial application. This paper presents a fast method to train neural networks with external dynamics for nonlinear dynamic system identification. The proposed method selects first a suitable set of neurons and trains the resulting network in a second step. This proposed procedure leads to faster convergence compared to standard training algorithms and comparable accuracy.

引用

页码：75 / 83

页数：9

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