Skip to main content
Article thumbnail
Location of Repository

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

By S.A. Billings and H.L. Wei


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:

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.