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Nonlinear model structure detection using optimum experimental design and orthogonal least squares

Hong, X. and Harris, C. J. (2001) Nonlinear model structure detection using optimum experimental design and orthogonal least squares. IEEE Transactions on Neural Networks, 12 (2). pp. 435-439. ISSN 1045-9227

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To link to this article DOI: 10.1109/72.914539

Abstract/Summary

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18502
Uncontrolled Keywords:A-optimality design criterion , composite cost function , efficient learning algorithm , forward orthogonal least squares , model adequacy , model approximation ability , model subset selection , nonlinear model structure detection , optimum experimental design , parameter estimation , subset selection cost function , variance minimization
Publisher:IEEE

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