DocumentCode
2313811
Title
The normalized radial basis function neural network
Author
Heimes, Felix ; Van Heuveln, Bram
Author_Institution
Lockheed Martin Control Syst., Johnson City, NY, USA
Volume
2
fYear
1998
fDate
11-14 Oct 1998
Firstpage
1609
Abstract
Presents a neural network called the normalized radial basis function (NRBF) neural network. The NRBF integrates techniques from two similar neural networks: the general regression neural network (GRNN) and the radial basis function (RBF) neural network. The NRBF is identical to the standard radial basis function (RBF) network except the hidden layer outputs are normalized before being passed through the output layer. The normalization of the hidden layer weights is shown to improve the extrapolation performance of the conventional RBF network. We have reason to believe that under normal circumstances the NRBF outperforms the RBF and the GRNN
Keywords
extrapolation; radial basis function networks; extrapolation performance; general regression neural network; hidden layer outputs; normalized radial basis function neural network; Cities and towns; Clustering algorithms; Control systems; Extrapolation; Least squares approximation; Neural networks; Radial basis function networks; Stock markets; Training data; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
Conference_Location
San Diego, CA
ISSN
1062-922X
Print_ISBN
0-7803-4778-1
Type
conf
DOI
10.1109/ICSMC.1998.728118
Filename
728118
Link To Document