Title :
Robust nonlinear model identification methods using forward regression
Author :
Hong, X. ; Harris, C.J. ; Chen, S. ; Sharkey, P.M.
Author_Institution :
Dept. of Cybern., Univ. of Reading, UK
fDate :
7/1/2003 12:00:00 AM
Abstract :
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.
Keywords :
computational complexity; identification; least squares approximations; nonlinear systems; stability; statistical analysis; A-optimality; D-optimality; LSA; PRESS statistic; computation efficiency; computational efficiency; forward regression; linear-in-the-parameters models; model generalization capability; parameter regularization; predicted residual square sum statistic; regularized orthogonal least squares algorithm; robust nonlinear model construction algorithms; robust nonlinear model identification methods; robust structural selective criteria; Approximation algorithms; Cost function; Design for experiments; Design optimization; Least squares approximation; Least squares methods; Parameter estimation; Predictive models; Robustness; Statistics;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/TSMCA.2003.809217