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A-optimality orthogonal forward regression algorithm using branch and bound

Hong, X., Chen, S. and Harris, C. J. (2008) A-optimality orthogonal forward regression algorithm using branch and bound. IEEE Transactions on Neural Networks, 19 (11). pp. 1961-1967. ISSN 1045-9227

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To link to this item DOI: 10.1109/tnn.2008.2003251


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
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15277
Uncontrolled Keywords:Branch and bound (BB), experimental design, forward regression, structure identification, LEAST-SQUARES, SYSTEM-IDENTIFICATION, MODELS, DESIGN

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