Referenced Items are not available for this document.
A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonlinear system identification and signal processing problem. A new forward orthogonal regression algorithm, with mutual information interference, is proposed for sparse model selection and parameter estimation. The new algorithm can be used to construct parsimonious linear-in-the-parameters models
Published in:
Neural Networks, IEEE Transactions on
(Volume:18
,
Issue:
1
)
Date of Publication: Jan. 2007
- Page(s):
- 306 - 310
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 9261036
- Digital Object Identifier :
- 10.1109/TNN.2006.886356
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 02 January 2007
- Issue Date :
- Jan. 2007
- Sponsored by :
- IEEE Computational Intelligence Society
- PubMed ID :
- 17278482
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