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Conditions for attaining the global minimum in maximum likelihood system identification

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Conditions for attaining the global minimum in maximum likelihood system identification. / Zou, Y.; Heath, W.P.
In: IFAC Proceedings Volumes (IFAC-PapersOnline), Vol. 42, No. 10, 21.04.2016, p. 1110-1115.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Zou, Y & Heath, WP 2016, 'Conditions for attaining the global minimum in maximum likelihood system identification', IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 42, no. 10, pp. 1110-1115. https://doi.org/10.3182/20090706-3-FR-2004.00184

APA

Zou, Y., & Heath, W. P. (2016). Conditions for attaining the global minimum in maximum likelihood system identification. IFAC Proceedings Volumes (IFAC-PapersOnline), 42(10), 1110-1115. https://doi.org/10.3182/20090706-3-FR-2004.00184

CBE

Zou Y, Heath WP. 2016. Conditions for attaining the global minimum in maximum likelihood system identification. IFAC Proceedings Volumes (IFAC-PapersOnline). 42(10):1110-1115. https://doi.org/10.3182/20090706-3-FR-2004.00184

MLA

Zou, Y. and W.P. Heath. "Conditions for attaining the global minimum in maximum likelihood system identification". IFAC Proceedings Volumes (IFAC-PapersOnline). 2016, 42(10). 1110-1115. https://doi.org/10.3182/20090706-3-FR-2004.00184

VancouverVancouver

Zou Y, Heath WP. Conditions for attaining the global minimum in maximum likelihood system identification. IFAC Proceedings Volumes (IFAC-PapersOnline). 2016 Apr 21;42(10):1110-1115. Epub 2010 Feb 19. doi: 10.3182/20090706-3-FR-2004.00184

Author

Zou, Y. ; Heath, W.P. / Conditions for attaining the global minimum in maximum likelihood system identification. In: IFAC Proceedings Volumes (IFAC-PapersOnline). 2016 ; Vol. 42, No. 10. pp. 1110-1115.

RIS

TY - JOUR

T1 - Conditions for attaining the global minimum in maximum likelihood system identification

AU - Zou, Y.

AU - Heath, W.P.

PY - 2016/4/21

Y1 - 2016/4/21

N2 - Maximum likelihood estimation(MLE) is a popular technique in both open and closed loop identification. However when the landscape of likelihood function has several local minima, gradient based optimization might end up with local convergence. To avoid this, various non-local-minimum conditions are derived in this paper. Here we consider different model structures, in particular Output-Error, ARMAX, and Box-Jenkins models

AB - Maximum likelihood estimation(MLE) is a popular technique in both open and closed loop identification. However when the landscape of likelihood function has several local minima, gradient based optimization might end up with local convergence. To avoid this, various non-local-minimum conditions are derived in this paper. Here we consider different model structures, in particular Output-Error, ARMAX, and Box-Jenkins models

U2 - 10.3182/20090706-3-FR-2004.00184

DO - 10.3182/20090706-3-FR-2004.00184

M3 - Erthygl

VL - 42

SP - 1110

EP - 1115

JO - IFAC Proceedings Volumes (IFAC-PapersOnline)

JF - IFAC Proceedings Volumes (IFAC-PapersOnline)

IS - 10

ER -