Adaptive orthogonal least squares learning algorithm for the radial basis function network

This paper presents an algorithm to select the parameters of a radial basis function network based on the orthogonal least squares (OLS) learning algorithm. To improve the OLS learning process, an additional procedure to modify the selected node's parameter during training is introduced. Using...

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Bibliographic Details
Main Authors: Eng-Siong Chang, Yang, H., Bos, S.
Format: Conference Proceeding
Language:English
Subjects:
Board of Directors
Computer networks
Equations
Fiber reinforced plastics
Gaussian processes
Identity-based encryption
Least squares approximation
Least squares methods
Radial basis function networks
Tiles
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Description
Summary:This paper presents an algorithm to select the parameters of a radial basis function network based on the orthogonal least squares (OLS) learning algorithm. To improve the OLS learning process, an additional procedure to modify the selected node's parameter during training is introduced. Using simulation results, we show that significant improvement to the selected model's performance can be achieved by the proposed algorithm.
ISSN:1089-3555
2379-2329
DOI:10.1109/NNSP.1996.548330