Message passing-based system identification for NARMAX models

Albert Podusenko; Semih Akbayrak; Ismail Șenöz; Maarten Schoukens; Wouter M. Kouw

doi:10.1109/CDC51059.2022.9992891

Message passing-based system identification for NARMAX models

Albert Podusenko, Semih Akbayrak, Ismail Șenöz, Maarten Schoukens, Wouter M. Kouw

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

4 Downloads (Pure)

Samenvatting

We present a variational Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph. Message passing allows us to obtain full posterior distributions for regression coefficients, precision parameters and noise instances by means of local computations distributed according to the factorization of the dynamic model. The posterior distributions are important to shaping the predictive distribution for outputs, and ultimately lead to superior model performance during 1-step ahead prediction and simulation.

Originele taal-2	Engels
Titel	2022 IEEE 61st Conference on Decision and Control (CDC)
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	7309-7314
Aantal pagina's	6
ISBN van elektronische versie	978-1-6654-6761-2
DOI's	https://doi.org/10.1109/CDC51059.2022.9992891
Status	Gepubliceerd - 10 jan. 2023
Evenement	2022 IEEE 61st Conference on Decision and Control (CDC) - The Marriott Cancún Collection, Cancun, Mexico Duur: 6 dec. 2022 → 9 dec. 2022 Congresnummer: 61 https://cdc2022.ieeecss.org/

Congres

Congres	2022 IEEE 61st Conference on Decision and Control (CDC)
Verkorte titel	CDC 2022
Land/Regio	Mexico
Stad	Cancun
Periode	6/12/22 → 9/12/22
Internet adres	https://cdc2022.ieeecss.org/

Toegang tot document

10.1109/CDC51059.2022.9992891

Andere bestanden en links

Link naar publicatie in Scopus

Citeer dit

@inproceedings{36dff31f6f5649759f81e02d64ff45b8,

title = "Message passing-based system identification for NARMAX models",

abstract = "We present a variational Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph. Message passing allows us to obtain full posterior distributions for regression coefficients, precision parameters and noise instances by means of local computations distributed according to the factorization of the dynamic model. The posterior distributions are important to shaping the predictive distribution for outputs, and ultimately lead to superior model performance during 1-step ahead prediction and simulation.",

author = "Albert Podusenko and Semih Akbayrak and Ismail Șen{\"o}z and Maarten Schoukens and Kouw, {Wouter M.}",

year = "2023",

month = jan,

day = "10",

doi = "10.1109/CDC51059.2022.9992891",

language = "English",

pages = "7309--7314",

booktitle = "2022 IEEE 61st Conference on Decision and Control (CDC)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "61st IEEE Conference on Decision and Control, CDC 2022, CDC 2022 ; Conference date: 06-12-2022 Through 09-12-2022",

url = "https://cdc2022.ieeecss.org/",

}

Podusenko, A , Akbayrak, S , Șenöz, I , Schoukens, M & Kouw, WM 2023, Message passing-based system identification for NARMAX models. in 2022 IEEE 61st Conference on Decision and Control (CDC)., 9992891, Institute of Electrical and Electronics Engineers, blz. 7309-7314, 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 6/12/22. https://doi.org/10.1109/CDC51059.2022.9992891

Message passing-based system identification for NARMAX models. / Podusenko, Albert ; Akbayrak, Semih ; Șenöz, Ismail et al.
2022 IEEE 61st Conference on Decision and Control (CDC). Institute of Electrical and Electronics Engineers, 2023. blz. 7309-7314 9992891.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Message passing-based system identification for NARMAX models

AU - Podusenko, Albert

AU - Akbayrak, Semih

AU - Șenöz, Ismail

AU - Schoukens, Maarten

AU - Kouw, Wouter M.

N1 - Conference code: 61

PY - 2023/1/10

Y1 - 2023/1/10

N2 - We present a variational Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph. Message passing allows us to obtain full posterior distributions for regression coefficients, precision parameters and noise instances by means of local computations distributed according to the factorization of the dynamic model. The posterior distributions are important to shaping the predictive distribution for outputs, and ultimately lead to superior model performance during 1-step ahead prediction and simulation.

AB - We present a variational Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph. Message passing allows us to obtain full posterior distributions for regression coefficients, precision parameters and noise instances by means of local computations distributed according to the factorization of the dynamic model. The posterior distributions are important to shaping the predictive distribution for outputs, and ultimately lead to superior model performance during 1-step ahead prediction and simulation.

UR - http://www.scopus.com/inward/record.url?scp=85147040156&partnerID=8YFLogxK

U2 - 10.1109/CDC51059.2022.9992891

DO - 10.1109/CDC51059.2022.9992891

M3 - Conference contribution

SP - 7309

EP - 7314

BT - 2022 IEEE 61st Conference on Decision and Control (CDC)

PB - Institute of Electrical and Electronics Engineers

T2 - 61st IEEE Conference on Decision and Control, CDC 2022

Y2 - 6 December 2022 through 9 December 2022

ER -

Message passing-based system identification for NARMAX models

Samenvatting

Congres

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit