M-estimator and D-optimality model construction using orthogonal forward regression
作者： X. Hong , S. Chen
DOI: 10.1109/TSMCB.2004.839910
关键词:
摘要: This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for structure selection and is based on an M-estimators of parameter estimates. M-estimator classical robust estimation technique to tackle bad data conditions such as outliers. Computationally, The can be derived iterative reweighted least squares (IRLS) algorithm. robustness criterion in experimental design ill-conditioning structure. (OFR), often the modified Gram-Schmidt procedure, efficient method incorporating simultaneously. basic idea proposed approach incorporate IRLS inner loop into procedure. In this manner, OFR parsimonious determination extended with improved performance via derivation inherent Numerical examples are included demonstrate effectiveness
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