Incorporating term selection into separable nonlinear least squares identification methods

Mohammad Rasouli; David Westwick; William Rosehart

doi:10.1109/CCECE.2007.228

Incorporating term selection into separable nonlinear least squares identification methods

Mohammad Rasouli, David Westwick, William Rosehart

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2007 Canadian Conference on Electrical and Computer Engineering, CCECD
Pages	892-895
Number of pages	4
DOIs	https://doi.org/10.1109/CCECE.2007.228
State	Published - 2007
Event	2007 Canadian Conference on Electrical and Computer Engineering, CCECD - Vancouver, BC, Canada Duration: Apr 22 2007 → Apr 26 2007

Publication series

Name	Canadian Conference on Electrical and Computer Engineering
ISSN (Print)	0840-7789

Other

Other	2007 Canadian Conference on Electrical and Computer Engineering, CCECD
Country/Territory	Canada
City	Vancouver, BC
Period	4/22/07 → 4/26/07

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/CCECE.2007.228

Cite this

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title = "Incorporating term selection into separable nonlinear least squares identification methods",

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.",

author = "Mohammad Rasouli and David Westwick and William Rosehart",

year = "2007",

doi = "10.1109/CCECE.2007.228",

language = "English (US)",

isbn = "1424410215",

series = "Canadian Conference on Electrical and Computer Engineering",

pages = "892--895",

booktitle = "2007 Canadian Conference on Electrical and Computer Engineering, CCECD",

note = "2007 Canadian Conference on Electrical and Computer Engineering, CCECD ; Conference date: 22-04-2007 Through 26-04-2007",

}

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., 4232887, Canadian Conference on Electrical and Computer Engineering, pp. 892-895, 2007 Canadian Conference on Electrical and Computer Engineering, CCECD, Vancouver, BC, Canada, 4/22/07. https://doi.org/10.1109/CCECE.2007.228

Incorporating term selection into separable nonlinear least squares identification methods. / Rasouli, Mohammad; Westwick, David; Rosehart, William.
2007 Canadian Conference on Electrical and Computer Engineering, CCECD. 2007. p. 892-895 4232887 (Canadian Conference on Electrical and Computer Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Westwick, David

AU - Rosehart, William

PY - 2007

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N2 - 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.

AB - 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.

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U2 - 10.1109/CCECE.2007.228

DO - 10.1109/CCECE.2007.228

M3 - Conference contribution

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SN - 9781424410217

T3 - Canadian Conference on Electrical and Computer Engineering

SP - 892

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BT - 2007 Canadian Conference on Electrical and Computer Engineering, CCECD

T2 - 2007 Canadian Conference on Electrical and Computer Engineering, CCECD

Y2 - 22 April 2007 through 26 April 2007

ER -

Incorporating term selection into separable nonlinear least squares identification methods

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