Title :
Modeling for Nonlinear Systems by Use of RBF Network
Author :
Qu, Liping ; Lu, Jianming ; Yahagi, Takashi ; Qu, Yongyin
Author_Institution :
BeiHua Univ., Jilin
fDate :
May 30 2007-June 1 2007
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;
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
DOI :
10.1109/ICCA.2007.4376568
