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).

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Description/Abstract

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
ISSNs: 1045-9227 (print)
1941-0093 (electronic)
Subjects: Q Science > QC Physics
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 266894
Date Deposited: 10 Nov 2008 11:09
Last Modified: 27 Mar 2014 20:12
Further Information:Google Scholar
ISI Citation Count:1
URI: http://eprints.soton.ac.uk/id/eprint/266894

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