Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method

Junhong Li; Weixing Zheng; Juping Gu; Liang Hua

doi:10.1109/ChiCC.2016.7553369

Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method

Junhong Li, Weixing Zheng, Juping Gu, Liang Hua

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.

Original language	English
Title of host publication	Proceedings of 2016 35th Chinese Control Conference (CCC 2016), Chengdu, China, 27-29 July 2016
Publisher	IEEE
Pages	1886-1890
Number of pages	5
ISBN (Print)	9789881563910
DOIs	https://doi.org/10.1109/ChiCC.2016.7553369
Publication status	Published - 2016
Event	Chinese Control Conference - Duration: 27 Jul 2016 → …

Publication series

Name
ISSN (Print)	1934-1768

Conference

Conference	Chinese Control Conference
Period	27/07/16 → …

Keywords

nonlinear systems
parameter estimation
stochastic processes

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10.1109/ChiCC.2016.7553369

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@inproceedings{7283af5975c94696948698f8e798df63,

title = "Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method",

abstract = "This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.",

keywords = "nonlinear systems, parameter estimation, stochastic processes",

author = "Junhong Li and Weixing Zheng and Juping Gu and Liang Hua",

year = "2016",

doi = "10.1109/ChiCC.2016.7553369",

language = "English",

isbn = "9789881563910",

publisher = "IEEE",

pages = "1886--1890",

booktitle = "Proceedings of 2016 35th Chinese Control Conference (CCC 2016), Chengdu, China, 27-29 July 2016",

note = "Chinese Control Conference ; Conference date: 27-07-2016",

}

Li, J, Zheng, W, Gu, J & Hua, L 2016, Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method. in Proceedings of 2016 35th Chinese Control Conference (CCC 2016), Chengdu, China, 27-29 July 2016. IEEE, pp. 1886-1890, Chinese Control Conference, 27/07/16. https://doi.org/10.1109/ChiCC.2016.7553369

Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method. / Li, Junhong; Zheng, Weixing; Gu, Juping et al.
Proceedings of 2016 35th Chinese Control Conference (CCC 2016), Chengdu, China, 27-29 July 2016. IEEE, 2016. p. 1886-1890.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method

AU - Li, Junhong

AU - Zheng, Weixing

AU - Gu, Juping

AU - Hua, Liang

PY - 2016

Y1 - 2016

N2 - This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.

AB - This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.

KW - nonlinear systems

KW - parameter estimation

KW - stochastic processes

UR - http://handle.uws.edu.au:8081/1959.7/uws:37553

UR - http://ccc2016.swjtu.edu.cn/236-1.shtml

U2 - 10.1109/ChiCC.2016.7553369

DO - 10.1109/ChiCC.2016.7553369

M3 - Conference Paper

SN - 9789881563910

SP - 1886

EP - 1890

BT - Proceedings of 2016 35th Chinese Control Conference (CCC 2016), Chengdu, China, 27-29 July 2016

PB - IEEE

T2 - Chinese Control Conference

Y2 - 27 July 2016

ER -

Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method

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