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Bayesian parameter identification in dynamic state space models using modified measurement equations

Abhinav, S and Manohar, CS (2015) Bayesian parameter identification in dynamic state space models using modified measurement equations. In: INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS, 71 . pp. 89-103.

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Official URL: http://dx.doi.org/10.1016/j.ijnonlinmec.2015.02.00...

Abstract

When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending-torsion coupled, geometrically non-linear building frame under earthquake support motions. (C) 2015 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
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Additional Information: Copy right for this article belongs to the PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
Keywords: STRUCTURAL-SYSTEM-IDENTIFICATION; UPDATING MODELS; SELECTION; UNCERTAINTIES
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 28 Apr 2015 07:16
Last Modified: 28 Apr 2015 07:16
URI: http://eprints.iisc.ernet.in/id/eprint/51416

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