On maximum likelihood identification of errors-in-variables models

G. Bottegal; R.S. Risuleo; M. Zamani; B. Ninness; H. Hjalmarsson

doi:10.1016/j.ifacol.2017.08.634

On maximum likelihood identification of errors-in-variables models

G. Bottegal, R.S. Risuleo, M. Zamani, B. Ninness, H. Hjalmarsson

Control Systems

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

3 Downloads (Pure)

Abstract

In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.

Original language	English
Pages (from-to)	2824-2829
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	50
Issue number	1
DOIs	https://doi.org/10.1016/j.ifacol.2017.08.634
Publication status	Published - 1 Jul 2017

Access to Document

10.1016/j.ifacol.2017.08.634

Cite this

@article{b9743ccbbdd04d02a0c2fbcacc027bb0,

title = "On maximum likelihood identification of errors-in-variables models",

abstract = "In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.",

author = "G. Bottegal and R.S. Risuleo and M. Zamani and B. Ninness and H. Hjalmarsson",

year = "2017",

month = jul,

day = "1",

doi = "10.1016/j.ifacol.2017.08.634",

language = "English",

volume = "50",

pages = "2824--2829",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - On maximum likelihood identification of errors-in-variables models

AU - Bottegal, G.

AU - Risuleo, R.S.

AU - Zamani, M.

AU - Ninness, B.

AU - Hjalmarsson, H.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.

AB - In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85031797320&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2017.08.634

DO - 10.1016/j.ifacol.2017.08.634

M3 - Conference article

AN - SCOPUS:85031797320

SN - 2405-8963

VL - 50

SP - 2824

EP - 2829

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 1

ER -

On maximum likelihood identification of errors-in-variables models

Abstract

Access to Document

Other files and links

Fingerprint

Cite this