DocumentCode
2004544
Title
Modeling for Nonlinear Systems by Use of RBF Network
Author
Qu, Liping ; Lu, Jianming ; Yahagi, Takashi ; Qu, Yongyin
Author_Institution
BeiHua Univ., Jilin
fYear
2007
fDate
May 30 2007-June 1 2007
Firstpage
1284
Lastpage
1289
Abstract
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.
Keywords
modelling; nonlinear systems; radial basis function networks; RBF network; high power DC graphitizing furnace; nonlinear systems modeling; radial basis function neural network; Automatic control; Convergence; Furnaces; Least squares approximation; Neural networks; Nonlinear control systems; Nonlinear systems; Power system modeling; Radial basis function networks; Vectors; DC Graphitizing Furnace; Direct Typical-Point Selection; Forgetting Factor; Least Square Method; RBF Neural Network;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location
Guangzhou
Print_ISBN
978-1-4244-0818-4
Electronic_ISBN
978-1-4244-0818-4
Type
conf
DOI
10.1109/ICCA.2007.4376568
Filename
4376568
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