Sparse Model Identification Using a L1 Regularized Orthogonal Forward Regression Algorithm with a Bootstrap Covariance Criterion
Pan , Y and Billings, S.A.
(2006)
Sparse Model Identification Using a L1 Regularized Orthogonal Forward Regression Algorithm with a Bootstrap Covariance Criterion.
Research Report.
ACSE Research Report 936
.
Department of Control Engineering, University of Sheffield
Abstract
This paper presents a new model identification method for parsimoniously selecting model terms and estimating the corresponding parameters of nonlinear dynamical systems. The generalization and prediction capability of the final identified model with the smallest model size is ensured by optimizing the model prediction error over an unseen data using parametric bootstrap covariance estimates.
Metadata
Item Type:
Monograph
Authors/Creators:
Pan , Y
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.
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