Support vector machine identification of output error Hammerstein models

Mujahed Al Dhaifallah; David T. Westwick

doi:10.3182/20110828-6-IT-1002.00585

Support vector machine identification of output error Hammerstein models

Mujahed Al Dhaifallah^*
, David T. Westwick

^*Corresponding author for this work

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

3 Scopus citations

Abstract

The ARX structure is often used in linear and nonlinear system identification because it is compact and linear in the variables. However, the ARX structure includes a noise model which shares the same poles as the deterministic system which is not always appropriate. In this paper, we consider the extension of an SVM based identification technique for Hammerstein models with ARX linear dynamics proposed by Dhaifallah and Westwick (2008 IFAC World Congress, pp:4999-5004) to include the output-error class of linear system models. The presented algorithm will be compared to the previous Hammerstein ARX approach using simulations.

Original language	English
Title of host publication	Proceedings of the 18th IFAC World Congress
Publisher	IFAC Secretariat
Pages	13948-13953
Number of pages	6
Edition	1 PART 1
ISBN (Print)	9783902661937
DOIs	https://doi.org/10.3182/20110828-6-IT-1002.00585
State	Published - 2011

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Number	1 PART 1
Volume	44
ISSN (Print)	1474-6670

Bibliographical note

Funding Information:
⋆ The authors would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals (KFUPM) under Research Grant JF100012.

Keywords

Hammerstein
Identification
Output error
Support vector machines

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.3182/20110828-6-IT-1002.00585

Cite this

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title = "Support vector machine identification of output error Hammerstein models",

abstract = "The ARX structure is often used in linear and nonlinear system identification because it is compact and linear in the variables. However, the ARX structure includes a noise model which shares the same poles as the deterministic system which is not always appropriate. In this paper, we consider the extension of an SVM based identification technique for Hammerstein models with ARX linear dynamics proposed by Dhaifallah and Westwick (2008 IFAC World Congress, pp:4999-5004) to include the output-error class of linear system models. The presented algorithm will be compared to the previous Hammerstein ARX approach using simulations.",

keywords = "Hammerstein, Identification, Output error, Support vector machines",

author = "\{Al Dhaifallah\}, Mujahed and Westwick, \{David T.\}",

note = "Funding Information: ⋆ The authors would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals (KFUPM) under Research Grant JF100012.",

year = "2011",

doi = "10.3182/20110828-6-IT-1002.00585",

language = "English",

isbn = "9783902661937",

series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",

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booktitle = "Proceedings of the 18th IFAC World Congress",

edition = "1 PART 1",

}

Support vector machine identification of output error Hammerstein models. / Al Dhaifallah, Mujahed; Westwick, David T.
Proceedings of the 18th IFAC World Congress. 1 PART 1. ed. IFAC Secretariat, 2011. p. 13948-13953 (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 44, No. 1 PART 1).

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

TY - GEN

T1 - Support vector machine identification of output error Hammerstein models

AU - Al Dhaifallah, Mujahed

AU - Westwick, David T.

N1 - Funding Information: ⋆ The authors would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals (KFUPM) under Research Grant JF100012.

PY - 2011

Y1 - 2011

N2 - The ARX structure is often used in linear and nonlinear system identification because it is compact and linear in the variables. However, the ARX structure includes a noise model which shares the same poles as the deterministic system which is not always appropriate. In this paper, we consider the extension of an SVM based identification technique for Hammerstein models with ARX linear dynamics proposed by Dhaifallah and Westwick (2008 IFAC World Congress, pp:4999-5004) to include the output-error class of linear system models. The presented algorithm will be compared to the previous Hammerstein ARX approach using simulations.

AB - The ARX structure is often used in linear and nonlinear system identification because it is compact and linear in the variables. However, the ARX structure includes a noise model which shares the same poles as the deterministic system which is not always appropriate. In this paper, we consider the extension of an SVM based identification technique for Hammerstein models with ARX linear dynamics proposed by Dhaifallah and Westwick (2008 IFAC World Congress, pp:4999-5004) to include the output-error class of linear system models. The presented algorithm will be compared to the previous Hammerstein ARX approach using simulations.

KW - Hammerstein

KW - Identification

KW - Output error

KW - Support vector machines

UR - https://www.scopus.com/pages/publications/84866773018

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DO - 10.3182/20110828-6-IT-1002.00585

M3 - Conference contribution

AN - SCOPUS:84866773018

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T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)

SP - 13948

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BT - Proceedings of the 18th IFAC World Congress

PB - IFAC Secretariat

ER -

Support vector machine identification of output error Hammerstein models

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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