Robust nonlinear identification without a priori model form assumptions

D. Joseph Mook

doi:10.23919/acc.1991.4791954

Robust nonlinear identification without a priori model form assumptions

D. Joseph Mook

Department of Mechanical and Aerospace Engineering

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

Abstract

A robust nonlinear identification technique, based on the minimum model error (MME) optimal estimation approach, is modified by a postestimation correlation procedure to essentially eliminate any need for the user to assume the form of the nonlinear model, in contrast to previous nonlinear identification approaches which usually require detailed assumptions about the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The examples indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Original language	English
Title of host publication	Proceedings of the American Control Conference
Publisher	Publ by American Automatic Control Council
Pages	3007-3012
Number of pages	6
ISBN (Print)	0879425652, 9780879425654
DOIs	https://doi.org/10.23919/acc.1991.4791954
State	Published - 1991
Event	Proceedings of the 1991 American Control Conference - Boston, MA, USA Duration: Jun 26 1991 → Jun 28 1991

Publication series

Name	Proceedings of the American Control Conference
Volume	3
ISSN (Print)	0743-1619

Conference

Conference	Proceedings of the 1991 American Control Conference
City	Boston, MA, USA
Period	06/26/91 → 06/28/91

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10.23919/acc.1991.4791954

Cite this

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title = "Robust nonlinear identification without a priori model form assumptions",

abstract = "A robust nonlinear identification technique, based on the minimum model error (MME) optimal estimation approach, is modified by a postestimation correlation procedure to essentially eliminate any need for the user to assume the form of the nonlinear model, in contrast to previous nonlinear identification approaches which usually require detailed assumptions about the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The examples indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.",

author = "Mook, \{D. Joseph\}",

year = "1991",

doi = "10.23919/acc.1991.4791954",

language = "English",

isbn = "0879425652",

series = "Proceedings of the American Control Conference",

publisher = "Publ by American Automatic Control Council",

pages = "3007--3012",

booktitle = "Proceedings of the American Control Conference",

note = "Proceedings of the 1991 American Control Conference ; Conference date: 26-06-1991 Through 28-06-1991",

}

Mook, DJ 1991, Robust nonlinear identification without a priori model form assumptions. in Proceedings of the American Control Conference. Proceedings of the American Control Conference, vol. 3, Publ by American Automatic Control Council, pp. 3007-3012, Proceedings of the 1991 American Control Conference, Boston, MA, USA, 06/26/91. https://doi.org/10.23919/acc.1991.4791954

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AB - A robust nonlinear identification technique, based on the minimum model error (MME) optimal estimation approach, is modified by a postestimation correlation procedure to essentially eliminate any need for the user to assume the form of the nonlinear model, in contrast to previous nonlinear identification approaches which usually require detailed assumptions about the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The examples indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

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U2 - 10.23919/acc.1991.4791954

DO - 10.23919/acc.1991.4791954

M3 - Conference contribution

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SN - 0879425652

SN - 9780879425654

T3 - Proceedings of the American Control Conference

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BT - Proceedings of the American Control Conference

PB - Publ by American Automatic Control Council

T2 - Proceedings of the 1991 American Control Conference

Y2 - 26 June 1991 through 28 June 1991

ER -

Robust nonlinear identification without a priori model form assumptions

Abstract

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