Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
Billings, S.A. and Wei, H.L.
(2006)
Sparse model identification using a forward orthogonal regression algorithm aided by mutual information.
Research Report.
ACSE Research Report no. 919
.
Automatic Control and Systems Engineering, University of Sheffield
Abstract
A sparse representations, with satisfactory approximation accuracy, is usually desirable in any nonlinear system identification and signal processing problem. A new forward orthogonal regression algorithm, with mutual information interference, is proposed for sparse model selection and parameter estimation. The new algorithm can be used to construct parsimonious linear-in-the-parameters regression models.
Metadata
Item Type:
Monograph
Authors/Creators:
Billings, S.A.
Wei, H.L.
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:
model selection, mutual information, orthogonal least squares, parameter estimation, radial basis function networks.
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