Improved system identification with renormalization group

Qing Guo Wang; Chao Yu; Yong Zhang

doi:10.1109/ICCA.2013.6565089

Improved system identification with renormalization group

Qing Guo Wang, Chao Yu, Yong Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least-squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.

Original language	English
Title of host publication	2013 10th IEEE International Conference on Control and Automation, ICCA 2013
Pages	878-883
Number of pages	6
DOIs	https://doi.org/10.1109/ICCA.2013.6565089
Publication status	Published - 2013
Externally published	Yes
Event	2013 10th IEEE International Conference on Control and Automation, ICCA 2013 - Hangzhou, China Duration: 12 Jun 2013 → 14 Jun 2013

Publication series

Name	IEEE International Conference on Control and Automation, ICCA
ISSN (Print)	1948-3449
ISSN (Electronic)	1948-3457

Conference

Conference	2013 10th IEEE International Conference on Control and Automation, ICCA 2013
Country/Territory	China
City	Hangzhou
Period	12/06/13 → 14/06/13

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

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10.1109/ICCA.2013.6565089

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@inproceedings{2c829556afca4d65a11624ce276f2bda,

title = "Improved system identification with renormalization group",

abstract = "This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least-squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.",

author = "Wang, {Qing Guo} and Chao Yu and Yong Zhang",

year = "2013",

doi = "10.1109/ICCA.2013.6565089",

language = "English",

isbn = "9781467347075",

series = "IEEE International Conference on Control and Automation, ICCA",

pages = "878--883",

booktitle = "2013 10th IEEE International Conference on Control and Automation, ICCA 2013",

note = "2013 10th IEEE International Conference on Control and Automation, ICCA 2013 ; Conference date: 12-06-2013 Through 14-06-2013",

}

Wang, QG, Yu, C & Zhang, Y 2013, Improved system identification with renormalization group. in 2013 10th IEEE International Conference on Control and Automation, ICCA 2013., 6565089, IEEE International Conference on Control and Automation, ICCA, pp. 878-883, 2013 10th IEEE International Conference on Control and Automation, ICCA 2013, Hangzhou, China, 12/06/13. https://doi.org/10.1109/ICCA.2013.6565089

Improved system identification with renormalization group. / Wang, Qing Guo; Yu, Chao; Zhang, Yong.
2013 10th IEEE International Conference on Control and Automation, ICCA 2013. 2013. p. 878-883 6565089 (IEEE International Conference on Control and Automation, ICCA).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yu, Chao

AU - Zhang, Yong

PY - 2013

Y1 - 2013

N2 - This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least-squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.

AB - This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least-squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.

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U2 - 10.1109/ICCA.2013.6565089

DO - 10.1109/ICCA.2013.6565089

M3 - Conference contribution

AN - SCOPUS:84882343915

SN - 9781467347075

T3 - IEEE International Conference on Control and Automation, ICCA

SP - 878

EP - 883

BT - 2013 10th IEEE International Conference on Control and Automation, ICCA 2013

T2 - 2013 10th IEEE International Conference on Control and Automation, ICCA 2013

Y2 - 12 June 2013 through 14 June 2013

ER -

Improved system identification with renormalization group

Abstract

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ASJC Scopus subject areas

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