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

Author

Chang, Eng-Siong ; Yang, Howard ; Bös, Siegfried

Author_Institution

Inst. of Syst. Sci., Nat. Univ. of Singapore, Singapore

fYear

1996

fDate

4-6 Sep 1996

Firstpage

3

Lastpage

12

Abstract

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

Keywords

adaptive systems; feedforward neural nets; iterative methods; learning (artificial intelligence); least squares approximations; parameter estimation; adaptive learning; iterative methods; linear regression model; model performance; node parameter selection; orthogonal least squares learning; radial basis function network; 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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop

Conference_Location

Kyoto

ISSN

1089-3555

Print_ISBN

0-7803-3550-3

Type

conf

DOI

10.1109/NNSP.1996.548330

Filename

548330