An Adaptive Orthogonal Least Squares Algorithm for Model Subset Selection and Nonlinear System Identification
Wei, H. L. and Billings, S.A.
(2005)
An Adaptive Orthogonal Least Squares Algorithm for Model Subset Selection and Nonlinear System Identification.
Research Report.
ACSE Research Report 884
.
Department of Control Engineering, University of Sheffield
Abstract
A new adaptive orthogonal least squares (AOLS) algorithm is proposed for model subset selection and nonlinear system identification. Model subset selection, or model structure detection, is a key step in any identification procedure and consists of detecting and selecting significant model terms from a redundant candidate model term set to determine a parsimonious final model............
Metadata
Item Type:
Monograph
Authors/Creators:
Wei, H. L.
Billings, S.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
information criteria, model subset selection, model structure detection nonlinear system identification, orthogonal least squares, prediction error sum of squares
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)