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

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

Abstract

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 = "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 - 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 - 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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