Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems

Junhong Li; Weixing Zheng; Yi Yang; Qing Zhang; Chen Li

doi:10.1109/WCICA.2016.7578499

Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems

Junhong Li, Weixing Zheng, Yi Yang, Qing Zhang, Chen Li

Western Sydney University

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

Abstract

![CDATA[This paper studies the parameter estimation problem of Hammerstein output error moving average (OEMA) systems. According to the maximum likelihood principle and iterative identification technique, a maximum likelihood least squares iterative identification (ML-LSI) algorithm is proposed. A numerical example is provided to verify the effectiveness of the proposed algorithm.]]

Original language	English
Title of host publication	Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), 12-15 June 2016, Guilin, China
Publisher	IEEE
Pages	1280-1284
Number of pages	5
ISBN (Print)	9781467384148
DOIs	https://doi.org/10.1109/WCICA.2016.7578499
Publication status	Published - 2016
Event	World Congress on Intelligent Control and Automation - Duration: 12 Jun 2016 → …

Conference

Conference	World Congress on Intelligent Control and Automation
Period	12/06/16 → …

Keywords

algorithms
iterative methods (mathematics)
least squares
parameter estimation

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10.1109/WCICA.2016.7578499

Cite this

@inproceedings{9baeb17946e944d8b84d3e4483f77819,

title = "Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems",

abstract = "![CDATA[This paper studies the parameter estimation problem of Hammerstein output error moving average (OEMA) systems. According to the maximum likelihood principle and iterative identification technique, a maximum likelihood least squares iterative identification (ML-LSI) algorithm is proposed. A numerical example is provided to verify the effectiveness of the proposed algorithm.]]",

keywords = "algorithms, iterative methods (mathematics), least squares, parameter estimation",

author = "Junhong Li and Weixing Zheng and Yi Yang and Qing Zhang and Chen Li",

year = "2016",

doi = "10.1109/WCICA.2016.7578499",

language = "English",

isbn = "9781467384148",

pages = "1280--1284",

booktitle = "Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), 12-15 June 2016, Guilin, China",

publisher = "IEEE",

note = "World Congress on Intelligent Control and Automation ; Conference date: 12-06-2016",

}

Li, J, Zheng, W, Yang, Y, Zhang, Q & Li, C 2016, Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems. in Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), 12-15 June 2016, Guilin, China. IEEE, pp. 1280-1284, World Congress on Intelligent Control and Automation, 12/06/16. https://doi.org/10.1109/WCICA.2016.7578499

Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems. / Li, Junhong; Zheng, Weixing; Yang, Yi et al.
Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), 12-15 June 2016, Guilin, China. IEEE, 2016. p. 1280-1284.

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

TY - GEN

T1 - Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems

AU - Li, Junhong

AU - Zheng, Weixing

AU - Yang, Yi

AU - Zhang, Qing

AU - Li, Chen

PY - 2016

Y1 - 2016

N2 - ![CDATA[This paper studies the parameter estimation problem of Hammerstein output error moving average (OEMA) systems. According to the maximum likelihood principle and iterative identification technique, a maximum likelihood least squares iterative identification (ML-LSI) algorithm is proposed. A numerical example is provided to verify the effectiveness of the proposed algorithm.]]

AB - ![CDATA[This paper studies the parameter estimation problem of Hammerstein output error moving average (OEMA) systems. According to the maximum likelihood principle and iterative identification technique, a maximum likelihood least squares iterative identification (ML-LSI) algorithm is proposed. A numerical example is provided to verify the effectiveness of the proposed algorithm.]]

KW - algorithms

KW - iterative methods (mathematics)

KW - least squares

KW - parameter estimation

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:38510

U2 - 10.1109/WCICA.2016.7578499

DO - 10.1109/WCICA.2016.7578499

M3 - Conference Paper

SN - 9781467384148

SP - 1280

EP - 1284

BT - Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), 12-15 June 2016, Guilin, China

PB - IEEE

T2 - World Congress on Intelligent Control and Automation

Y2 - 12 June 2016

ER -

Maximum likelihood least squares iterative identification algorithm for Hammerstein output error moving average systems

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