Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification

Pingli Lu; Ying Yang; Wenbo Ma

Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification

Pingli Lu^*, Ying Yang, Wenbo Ma

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

In this paper, a new method of fuzzy identification based on fuzzy clustering and recursive least square is proposed. The membership degree of each given pattern is calculated by using fast fuzzy clustering algorithm and the consequent parameters are identified by recursive least square. It is shown that the computer CPU time has been greatly saved compared with fuzzy c-means clustering method. A numerical example is given at the end of the paper to demonstrate the applicability and validity of the proposed method.

Original language	English
Title of host publication	Proceedings of the 2006 American Control Conference
Pages	5049-5052
Number of pages	4
Publication status	Published - 2006
Externally published	Yes
Event	2006 American Control Conference - Minneapolis, MN, United States Duration: 14 Jun 2006 → 16 Jun 2006

Publication series

Name	Proceedings of the American Control Conference
Volume	2006
ISSN (Print)	0743-1619

Conference

Conference	2006 American Control Conference
Country/Territory	United States
City	Minneapolis, MN
Period	14/06/06 → 16/06/06

Cite this

@inproceedings{ee3b64a22e5448f68b77ab5ec142cbd8,

title = "Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification",

abstract = "In this paper, a new method of fuzzy identification based on fuzzy clustering and recursive least square is proposed. The membership degree of each given pattern is calculated by using fast fuzzy clustering algorithm and the consequent parameters are identified by recursive least square. It is shown that the computer CPU time has been greatly saved compared with fuzzy c-means clustering method. A numerical example is given at the end of the paper to demonstrate the applicability and validity of the proposed method.",

author = "Pingli Lu and Ying Yang and Wenbo Ma",

year = "2006",

language = "English",

isbn = "1424402107",

series = "Proceedings of the American Control Conference",

pages = "5049--5052",

booktitle = "Proceedings of the 2006 American Control Conference",

note = "2006 American Control Conference ; Conference date: 14-06-2006 Through 16-06-2006",

}

Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification. / Lu, Pingli; Yang, Ying; Ma, Wenbo.
Proceedings of the 2006 American Control Conference. 2006. p. 5049-5052 1657523 (Proceedings of the American Control Conference; Vol. 2006).

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

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AU - Yang, Ying

AU - Ma, Wenbo

PY - 2006

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N2 - In this paper, a new method of fuzzy identification based on fuzzy clustering and recursive least square is proposed. The membership degree of each given pattern is calculated by using fast fuzzy clustering algorithm and the consequent parameters are identified by recursive least square. It is shown that the computer CPU time has been greatly saved compared with fuzzy c-means clustering method. A numerical example is given at the end of the paper to demonstrate the applicability and validity of the proposed method.

AB - In this paper, a new method of fuzzy identification based on fuzzy clustering and recursive least square is proposed. The membership degree of each given pattern is calculated by using fast fuzzy clustering algorithm and the consequent parameters are identified by recursive least square. It is shown that the computer CPU time has been greatly saved compared with fuzzy c-means clustering method. A numerical example is given at the end of the paper to demonstrate the applicability and validity of the proposed method.

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M3 - Conference contribution

AN - SCOPUS:34047225737

SN - 1424402107

SN - 9781424402106

T3 - Proceedings of the American Control Conference

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BT - Proceedings of the 2006 American Control Conference

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Y2 - 14 June 2006 through 16 June 2006

ER -

Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification

Abstract

Publication series

Conference

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