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A robust nonlinear identification algorithm using PRESS statistic and forward regression
X Hong1, P M Sharkey, K Warwick
1Dept. of Cybern., Univ. of Reading, UK.
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.
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