Incorporating term selection into separable nonlinear least squares identification methods

Mohammad Rasouli, David Westwick, William Rosehart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2007 Canadian Conference on Electrical and Computer Engineering, CCECD
Pages892-895
Number of pages4
DOIs
Publication statusPublished - Dec 1 2007
Event2007 Canadian Conference on Electrical and Computer Engineering, CCECD - Vancouver, BC, Canada
Duration: Apr 22 2007Apr 26 2007

Other

Other2007 Canadian Conference on Electrical and Computer Engineering, CCECD
CountryCanada
CityVancouver, BC
Period4/22/074/26/07

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Hardware and Architecture

Cite this

Rasouli, M., Westwick, D., & Rosehart, W. (2007). Incorporating term selection into separable nonlinear least squares identification methods. In 2007 Canadian Conference on Electrical and Computer Engineering, CCECD (pp. 892-895). [4232887] https://doi.org/10.1109/CCECE.2007.228