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Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

Hong, X., Sharkey, P. M. and Warwick, K. (2003) Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic. IEE Proceedings-Control Theory and Applications, 150 (3). pp. 245-254. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:20030311

Abstract/Summary

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. 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. Numerical examples are used to demonstrate the efficacy of the algorithm.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15288
Uncontrolled Keywords:ORTHOGONAL LEAST-SQUARES, BASIS FUNCTION NETWORKS, IDENTIFICATION, DESIGN, SYSTEMS
Publisher:IET

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