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 :
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