Title :
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
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.886356
