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Identification of hysteretic systems using NARX models, part II: a Bayesian approach

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Identification of hysteretic systems using NARX models, part II : a Bayesian approach. / Worden, K.; Barthorpe, R. J.; Hensman, J. J.

Conference Proceedings of the Society for Experimental Mechanics Series. Vol. 4 2012. p. 57-65.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Worden, K, Barthorpe, RJ & Hensman, JJ 2012, Identification of hysteretic systems using NARX models, part II: a Bayesian approach. in Conference Proceedings of the Society for Experimental Mechanics Series. vol. 4, pp. 57-65, 30th IMAC, A Conference on Structural Dynamics, 2012, Jacksonville, FL, United States, 30/01/12. https://doi.org/10.1007/978-1-4614-2431-4_6

APA

Worden, K., Barthorpe, R. J., & Hensman, J. J. (2012). Identification of hysteretic systems using NARX models, part II: a Bayesian approach. In Conference Proceedings of the Society for Experimental Mechanics Series (Vol. 4, pp. 57-65) https://doi.org/10.1007/978-1-4614-2431-4_6

Vancouver

Worden K, Barthorpe RJ, Hensman JJ. Identification of hysteretic systems using NARX models, part II: a Bayesian approach. In Conference Proceedings of the Society for Experimental Mechanics Series. Vol. 4. 2012. p. 57-65 https://doi.org/10.1007/978-1-4614-2431-4_6

Author

Worden, K. ; Barthorpe, R. J. ; Hensman, J. J. / Identification of hysteretic systems using NARX models, part II : a Bayesian approach. Conference Proceedings of the Society for Experimental Mechanics Series. Vol. 4 2012. pp. 57-65

Bibtex

@inproceedings{c76867d13f864e599b54bdd27e562440,
title = "Identification of hysteretic systems using NARX models, part II: a Bayesian approach",
abstract = "Following on from the first part of this short sequence, this paper will investigate the use of a Bayesian methodology for the identification of Bouc-Wen hysteretic systems by NARX models. The approach - based on Markov Chain Monte Carlo - offers a number of advantages over the evolutionary approach of the first paper. Among them are the ability to sample from the probability density functions of the parameters in order to develop nonparametric estimators and the possibility of selecting model terms in a principled manner. The paper will investigate the use of the Deviance Information Criterion (DIC) as a means of selecting model terms, specifically the special basis functions developed for the Bouc-Wen system in Part I. Results for simulated data will be given.",
keywords = "Bayesian inference, Hysteresis, Markov chain monte carlo (MCMC), Nonlinear system identification, The bouc-wen model",
author = "K. Worden and Barthorpe, {R. J.} and Hensman, {J. J.}",
year = "2012",
doi = "10.1007/978-1-4614-2431-4_6",
language = "English",
isbn = "9781461424307",
volume = "4",
pages = "57--65",
booktitle = "Conference Proceedings of the Society for Experimental Mechanics Series",
note = "30th IMAC, A Conference on Structural Dynamics, 2012 ; Conference date: 30-01-2012 Through 02-02-2012",

}

RIS

TY - GEN

T1 - Identification of hysteretic systems using NARX models, part II

T2 - 30th IMAC, A Conference on Structural Dynamics, 2012

AU - Worden, K.

AU - Barthorpe, R. J.

AU - Hensman, J. J.

PY - 2012

Y1 - 2012

N2 - Following on from the first part of this short sequence, this paper will investigate the use of a Bayesian methodology for the identification of Bouc-Wen hysteretic systems by NARX models. The approach - based on Markov Chain Monte Carlo - offers a number of advantages over the evolutionary approach of the first paper. Among them are the ability to sample from the probability density functions of the parameters in order to develop nonparametric estimators and the possibility of selecting model terms in a principled manner. The paper will investigate the use of the Deviance Information Criterion (DIC) as a means of selecting model terms, specifically the special basis functions developed for the Bouc-Wen system in Part I. Results for simulated data will be given.

AB - Following on from the first part of this short sequence, this paper will investigate the use of a Bayesian methodology for the identification of Bouc-Wen hysteretic systems by NARX models. The approach - based on Markov Chain Monte Carlo - offers a number of advantages over the evolutionary approach of the first paper. Among them are the ability to sample from the probability density functions of the parameters in order to develop nonparametric estimators and the possibility of selecting model terms in a principled manner. The paper will investigate the use of the Deviance Information Criterion (DIC) as a means of selecting model terms, specifically the special basis functions developed for the Bouc-Wen system in Part I. Results for simulated data will be given.

KW - Bayesian inference

KW - Hysteresis

KW - Markov chain monte carlo (MCMC)

KW - Nonlinear system identification

KW - The bouc-wen model

U2 - 10.1007/978-1-4614-2431-4_6

DO - 10.1007/978-1-4614-2431-4_6

M3 - Conference contribution/Paper

AN - SCOPUS:84861745377

SN - 9781461424307

VL - 4

SP - 57

EP - 65

BT - Conference Proceedings of the Society for Experimental Mechanics Series

Y2 - 30 January 2012 through 2 February 2012

ER -