Accessibility navigation


Robust nonlinear model identification methods using forward regression

Hong, X., Harris, C. J., Chen, S. and Sharkey, P. M. (2003) Robust nonlinear model identification methods using forward regression. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, 33 (4). pp. 514-523. ISSN 1083-4427

Full text not archived in this repository.

To link to this article DOI: 10.1109/tsmca.2003.809217

Abstract/Summary

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15282
Uncontrolled Keywords:cross validation, experimental design, forward regression, generalization, structure identification, ORTHOGONAL LEAST-SQUARES, BASIS FUNCTION NETWORKS, CONSTRUCTION, DESIGN, ALGORITHM

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation