Title :
Incorporating Term Selection Into Separable Nonlinear Least Squares Identification Methods
Author :
Rasouli, Mohammad ; Westwick, David ; Rosehart, William
Abstract :
In this paper, a method for the integration of the Least absolute shrinkage and selection operator (Lasso) into Separable Nonlinear Least Squares (SNLS) algorithms is presented. Lasso is reformulated as an equality constrained linear regression. The original SNLS problem is then solved subject to the resulting equality constraints. Simulations using the proposed algorithm to fit a Laguerre model to the output of a linear system are used to demonstrate its performance.
Keywords :
least squares approximations; parameter estimation; regression analysis; Laguerre model; equality constrained linear regression; integration; least absolute shrinkage; linear system; nonlinear least squares identification methods; selection operator; separable nonlinear least squares algorithms; term selection; Computational modeling; Filter bank; Least squares methods; Linear regression; Linear systems; Low pass filters; Nonlinear filters; Parameter estimation; System identification; Vectors;
Conference_Titel :
Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
1-4244-1020-7
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2007.228
