Modeling for nonlinear systems by use of RBF network

被引：0

作者：

Qu, Liping ^{[1
,2
]}

Lu, Jianming ^{[2
]}

Yahagi, Takashi ^{[2
]}

Qu, Yongyin ^{[1
]}

机构：

[1] BeiHua Univ, Informat Engn Sch, Jilin, Peoples R China

[2] Chiba Univ, Grad Sch Sci & Technol, Chiba, Japan

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年

关键词：

RBF neural network; DC graphitizing furnace; direct typical-point selection; least square method; forgetting factor;

D O I：

暂无

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 presents a means to make the model for nonlinear systems based on Radial Basis Function Neural Network (RBFNN).As a example, the high power DC graphitizing furnace is analyzed, and the RBF model of the system is constructed from experiments or simulations. The procedures for training the model are described along with discussions on error. All the simulated results show that the discussed approaches are effective.

引用

页码：1872 / +

页数：3

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