DocumentCode :
1166507
Title :
A robust nonlinear identification algorithm using PRESS statistic and forward regression
Author :
Hong, X. ; Sharkey, P.M. ; Warwick, K.
Author_Institution :
Dept. of Cybern., Univ. of Reading, UK
Volume :
14
Issue :
2
fYear :
2003
fDate :
3/1/2003 12:00:00 AM
Firstpage :
454
Lastpage :
458
Abstract :
This paper introduces a new robust nonlinear identification algorithm using the predicted residual sums of squares (PRESS) statistic and forward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.
Keywords :
function approximation; generalisation (artificial intelligence); identification; radial basis function networks; statistical analysis; time series; PRESS statistic; cross validation; forward regression; function approximation; generalization; nonlinear model; orthogonalization; predicted residual sums of squares; radial basis function network; structure identification; time series; Cost function; Iterative algorithms; Least squares approximation; Neural networks; Parameter estimation; Particle measurements; Prediction algorithms; Predictive models; Robustness; Statistics;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2003.809422
Filename :
1189645
Link To Document :
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