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A robust nonlinear identification algorithm using PRESS statistic and forward regression

Hong, X., Sharkey, P. M. and Warwick, K. (2003) A robust nonlinear identification algorithm using PRESS statistic and forward regression. IEEE Transactions on Neural Networks, 14 (2). pp. 454-458. ISSN 1045-9227

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To link to this item DOI: 10.1109/tnn.2003.809422

Abstract/Summary

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15287
Uncontrolled Keywords:cross validation, forward regreession, orthogonalization, radial basis, function (RBF) network, structure identification, ORTHOGONAL LEAST-SQUARES, CONSTRUCTION, DESIGN

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