A modified orthogonal forward regression least-squares
algorithm for system modelling from noisy regressors
Guo, L.Z. and Billings, S.A.
(2005)
A modified orthogonal forward regression least-squares
algorithm for system modelling from noisy regressors.
Research Report.
ACSE Research Report no. 911
.
Automatic Control and Systems Engineering, University of Sheffield
Abstract
In this paper, a modified Orthogonal Forward Regression (OFR) least-squares algorithm is presented for system identification and modelling from noisey regressors. Under the asumption that the energy and signal-to-noise ratio (SNR) of the signals are known or can be estimated , it is shown that unbiased estimates of the Error Reducation Ratios (ERRs) and the parameters can be obtained in each forward regression step. Examples are provided to illustrate the proposed approach.
Metadata
Item Type:
Monograph
Authors/Creators:
Guo, L.Z.
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.
![[thumbnail of 911.pdf]](https://eprints.whiterose.ac.uk/style/images/fileicons/text.png "911.pdf")
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)