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Nonlinear Model Updating Using Recursive and Batch Bayesian Methods
dc.creator | Song M., Astroza R., Ebrahimian H., Moaveni B., Papadimitriou C. | en |
dc.date.accessioned | 2023-01-31T09:59:01Z | |
dc.date.available | 2023-01-31T09:59:01Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1007/978-3-030-47638-0_31 | |
dc.identifier.isbn | 9783030487782 | |
dc.identifier.issn | 21915644 | |
dc.identifier.uri | http://hdl.handle.net/11615/79193 | |
dc.description.abstract | This paper studies the performance of recursive and batch Bayesian methods for nonlinear model updating. Unscented Kalman filter (UKF) is selected to represent the recursive Bayesian method, and two UKF approaches are investigated and compared, i.e., non-adaptive UKF and adaptive UKF. The proposed new adaptive filter, forgetting factor adaptive UKF, estimates the model parameters and measurement noise covariance in an online manner. The forgetting factor adaptive UKF is based on the principle of matching the covariance of residuals to its theoretical values by updating the measurement noise covariance. The performance of non-adaptive UKF, adaptive UKF and batch Bayesian method are investigated when applied to a numerical nonlinear 3-story 3-bay steel frame structure for parameter estimation of material properties. Different types of modeling errors are considered in the 21 updating models to study the effects of modeling errors on model updating. It is found that adaptive UKF approach provides the most accurate parameter estimations, while batch Bayesian approach gives the smallest errors on response predictions. © 2020, The Society for Experimental Mechanics, Inc. | en |
dc.language.iso | en | en |
dc.source | Conference Proceedings of the Society for Experimental Mechanics Series | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120435658&doi=10.1007%2f978-3-030-47638-0_31&partnerID=40&md5=5ed8264304008d8ad245df1df288c5f0 | |
dc.subject | Adaptive filtering | en |
dc.subject | Adaptive filters | en |
dc.subject | Bayesian networks | en |
dc.subject | Errors | en |
dc.subject | Kalman filters | en |
dc.subject | Nonlinear systems | en |
dc.subject | Numerical methods | en |
dc.subject | Parameter estimation | en |
dc.subject | Structural dynamics | en |
dc.subject | Structural frames | en |
dc.subject | Adaptive unscented kalman filter | en |
dc.subject | Bayesian methods | en |
dc.subject | Bayesian model updating | en |
dc.subject | Measurement Noise | en |
dc.subject | Measurement noise covariance | en |
dc.subject | Model errors | en |
dc.subject | Noise covariance | en |
dc.subject | Nonlinear model updating | en |
dc.subject | Unscented Kalman Filter | en |
dc.subject | Spurious signal noise | en |
dc.subject | Springer | en |
dc.title | Nonlinear Model Updating Using Recursive and Batch Bayesian Methods | en |
dc.type | conferenceItem | en |
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