A robust nonlinear identification algorithm using PRESS statistic and forward regression

Authors:
X. Hong;P. M. Sharkey;K. Warwick
Affiliations:
Dept. of Cybern., Univ. of Reading, UK;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2003

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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.