A-optimality orthogonal forward regression algorithm using branch and bound.
Neural Networks, IEEE Transactions（2008）
摘要
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.
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关键词
neural network,regression step,decision boundary,statistical testing,computational cost,branch and bound (bb),a-optimality composite cost function,tree searching,adaptive diagnostics test,regression analysis,forward regression,candidate regressors,composite cost function,structure identification,bb principle,orthogonal forward regression algorithm,proposed algorithm,a-optimality orthogonal forward regression,experimental design,design of experiments,model structure determination,a-optimality ofr algorithm,branch-and-bound principle,a-optimality experimental design,neural nets,indexing terms,linear programming,cost function,neural networks,nonlinear systems,branch and bound,convergence,testing,parallel processing
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