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Sparse model identification using a forward orthogonal\ud regression algorithm aided by mutual information

By S.A. Billings and H.L. Wei

Abstract

A sparse representation, with satisfactory approximation accuracy,\ud is usually desirable in any nonlinear system identification and signal\ud processing problem. A new forward orthogonal regression algorithm, with\ud mutual information interference, is proposed for sparse model selection and\ud parameter estimation. The new algorithm can be used to construct parsimonious\ud linear-in-the-parameters models

Publisher: IEEE
Year: 2007
OAI identifier: oai:eprints.whiterose.ac.uk:1970

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