title: A-optimality orthogonal forward regression algorithm using branch and bound.
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.
authors.affiliation: School of Systems Engineering, University of Reading, Reading RG661Y, UK. x.hong@reading.ac.uk
authors.forename: Xia
authors.initials: X
authors.lastname: Hong
authors.forename: Sheng
authors.initials: S
authors.lastname: Chen
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Harris
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012044:Regression Analysis