A-optimality orthogonal forward regression algorithm using branch and bound

Hong, Xia, Chen, Sheng and Harris, Chris J. (2008) A-optimality orthogonal forward regression algorithm using branch and bound. IEEE Transactions on Neural Networks, 19, (11), 1961-1967. (doi:10.1109/TNN.2008.2003251). (PMID:19000965).


[img] PDF (manuscript) - Version of Record
Download (543Kb)


In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TNN.2008.2003251
ISSNs: 1045-9227 (print)
1941-0093 (electronic)
Subjects: Q Science > QC Physics
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 266894
Accepted Date and Publication Date:
November 2008Published
Date Deposited: 10 Nov 2008 11:09
Last Modified: 31 Mar 2016 14:13
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/266894

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics