Abstract
Identification algorithms based on the well-known linear least squares methods of<br/>gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified<br/>Gram-Schmidt, Householder transformation, Givens method, and singular value<br/>decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt,<br/>and Householder transformation algorithms are then extended to combine<br/>structure determination, or which terms to include in the model, and parameter<br/>estimation in a very simple and efficient manner for a class of multivariable<br/>discrete-time non-linear stochastic systems which are linear in the parameters.
Keywords
Cholesky decompositionMathematicsQR decompositionSingular value decompositionApplied mathematicsLeast-squares function approximationLinear least squaresGeneralized least squaresTransformation (genetics)AlgorithmMathematical optimizationStatisticsEigenvalues and eigenvectors
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 50
- Issue
- 5
- Pages
- 1873-1896
- Citations
- 1530
- Access
- Closed
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Cite This
Sheng Chen,
S.A. Billings,
Wan Luo
(1989).
Orthogonal least squares methods and their application to non-linear system identification.
International Journal of Control
, 50
(5)
, 1873-1896.
https://doi.org/10.1080/00207178908953472
Identifiers
- DOI
- 10.1080/00207178908953472