Nonlinear model structure detection using optimum experimental design and orthogonal least squares

Abstract

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion… (More)
DOI: 10.1109/72.914539

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