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Parameter estimation based on stacked regression and evolutionary algorithms

Hong, X. and Billings, S. A. (1999) Parameter estimation based on stacked regression and evolutionary algorithms. IEE Proceedings-Control Theory and Applications, 146 (5). pp. 406-414. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:19990505

Abstract/Summary

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors are used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18510
Uncontrolled Keywords:Canadian lynx time series, MDE, OLS algorithm, PRESS prediction errors, concise model structure, cross-validated prediction error minimisation, cross-validation, evolutionary algorithms, forward orthogonal least-squares algorithm, linear-in-the-parameter models, mean dispersion error, parameter estimation, parsimony, stacked regression, sunspot time series
Publisher:IET

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