Sparse Model Identification Using a Forward Orthogonal Regression Algorithm Aided by Mutual Information

Author

Billings, Stephen A. ; Wei, Hua-Liang

Author_Institution

Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield

Volume

18

Issue

1

fYear

2007

Firstpage

306

Lastpage

310

Abstract

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

Keywords

nonlinear systems; parameter estimation; regression analysis; signal processing; forward orthogonal regression algorithm; mutual information interference; nonlinear system identification; parameter estimation; signal processing; sparse model identification; Approximation algorithms; Dictionaries; Function approximation; Interference; Least squares approximation; Mutual information; Nonlinear systems; Parameter estimation; Signal processing; Signal processing algorithms; Model selection; mutual information; orthogonal least squares (OLS); parameter estimation; Algorithms; Artificial Intelligence; Computer Simulation; Databases, Factual; Information Storage and Retrieval; Models, Statistical; Pattern Recognition, Automated; Regression Analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.886356

Filename

4049812