Switched linear system identification based on bounded-switching clustering

Mohammad Gorji Sefidmazgi; Mina Moradi Kordmahalleh; Abdollah Homaifar; Ali Karimoddini

doi:10.1109/ACC.2015.7170995

Switched linear system identification based on bounded-switching clustering

Mohammad Gorji Sefidmazgi, Mina Moradi Kordmahalleh, Abdollah Homaifar, Ali Karimoddini

Industrial and systems engineering with North Carolina A&T State University

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

Abstract

This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant in time. Optimal number and order of linear sub-systems as well as the number of switches are selected using Akaike Information Criterion. The performance of the algorithm is evaluated through simulations. Parameters and structures of switched systems are found accurately in the presence of noise.

Original language	English
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1806-1811
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7170995
State	Published - Jul 28 2015
Externally published	Yes
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Conference

Conference	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	07/1/15 → 07/3/15

Access to Document

10.1109/ACC.2015.7170995

Cite this

Sefidmazgi, M. G., Kordmahalleh, M. M., Homaifar, A., & Karimoddini, A. (2015). Switched linear system identification based on bounded-switching clustering. In ACC 2015 - 2015 American Control Conference (pp. 1806-1811). Article 7170995 (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7170995

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title = "Switched linear system identification based on bounded-switching clustering",

abstract = "This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant in time. Optimal number and order of linear sub-systems as well as the number of switches are selected using Akaike Information Criterion. The performance of the algorithm is evaluated through simulations. Parameters and structures of switched systems are found accurately in the presence of noise.",

author = "Sefidmazgi, \{Mohammad Gorji\} and Kordmahalleh, \{Mina Moradi\} and Abdollah Homaifar and Ali Karimoddini",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

year = "2015",

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day = "28",

doi = "10.1109/ACC.2015.7170995",

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Sefidmazgi, MG, Kordmahalleh, MM, Homaifar, A & Karimoddini, A 2015, Switched linear system identification based on bounded-switching clustering. in ACC 2015 - 2015 American Control Conference., 7170995, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 1806-1811, 2015 American Control Conference, ACC 2015, Chicago, United States, 07/1/15. https://doi.org/10.1109/ACC.2015.7170995

Switched linear system identification based on bounded-switching clustering. / Sefidmazgi, Mohammad Gorji; Kordmahalleh, Mina Moradi; Homaifar, Abdollah et al.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1806-1811 7170995 (Proceedings of the American Control Conference; Vol. 2015-July).

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

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AU - Homaifar, Abdollah

AU - Karimoddini, Ali

PY - 2015/7/28

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N2 - This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant in time. Optimal number and order of linear sub-systems as well as the number of switches are selected using Akaike Information Criterion. The performance of the algorithm is evaluated through simulations. Parameters and structures of switched systems are found accurately in the presence of noise.

AB - This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant in time. Optimal number and order of linear sub-systems as well as the number of switches are selected using Akaike Information Criterion. The performance of the algorithm is evaluated through simulations. Parameters and structures of switched systems are found accurately in the presence of noise.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 American Control Conference, ACC 2015

Y2 - 1 July 2015 through 3 July 2015

ER -

Switched linear system identification based on bounded-switching clustering

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