Dynamic multilayer neural networks for nonlinear system on-line identification

被引：5

作者：

Yu, W ^{[1
]}

Poznyak, AS ^{[1
]}

Sanchez, EN ^{[1
]}

机构：

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

来源：

PROCEEDINGS OF THE 2000 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL | 2000年

关键词：

dynamic neural networks; multilayer perceptrons; system identification;

D O I：

10.1109/ISIC.2000.882894

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To identify on-line a quite general class of nonlinear systems, this paper proposes a new stable learning law of the Dynamic Multilayer Neural Networks (DMNN). A Lyapunov-like analysis is used to derive this stable learning procedure for the hidden layer as well as for the output layer. An algebraic Riccati equation is considered to construct a bound for the identification error. The suggested learning algorithm is similar to the well-known backpropagation rule of the static multilayer perceptrons but with an additional term which assure the property of global asymptotic stability for the identification error. Two numerical examples illustrate the effectiveness of the suggested new learning laws.

引用

页码：25 / 30

页数：6

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