DocumentCode
1844921
Title
Research on the Non-Linear Function Fitting of RBF Neural Network
Author
Liu Jin-Yue ; Zhu Bao-Ling
Author_Institution
Comput. & Inf. Technol. Coll., Northeast Pet. Univ., Daqing, China
fYear
2013
fDate
21-23 June 2013
Firstpage
842
Lastpage
845
Abstract
By the simulation instance, this paper carries out a comparative research of the function approximation ability of BP network and RBF network, and analyzes the fitting accuracy and time efficiency of these two artificial neural networks when they are used to accomplish nonlinear function fitting under the specified parameters. The results show that the function approximation ability of BP network is superior to BR network in many ways.
Keywords
backpropagation; function approximation; nonlinear functions; radial basis function networks; BP network; RBF neural network; artificial neural networks; function approximation ability; nonlinear function fitting; simulation instance; Biological neural networks; Function approximation; Least squares approximations; Radial basis function networks; Training; BP neural network; RBF neural network; function approximation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on
Conference_Location
Shiyang
Type
conf
DOI
10.1109/ICCIS.2013.226
Filename
6643142
Link To Document