On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification

•An output only analysis of structural systems under non-Gaussian excitation is shown.•The GPLFM is shown for joint input-state-parameter identification for wave loading.•MCMC solutions of the model recover posterior distributions of parameters in the system.•The extension of the model for use in mo...

Full description

Saved in:

Bibliographic Details
Published in:	Mechanical systems and signal processing Vol. 140; p. 106580
Main Authors:	Rogers, T.J., Worden, K., Cross, E.J.
Format:	Journal Article
Language:	English
Published:	Berlin Elsevier Ltd 01.06.2020 Elsevier BV
Subjects:	Bayesian Bayesian analysis Gaussian process Identification Latent force model Operational modal analysis Parameter estimation Parameter identification Random noise Signal processing State space models Statistical inference Structural health monitoring System identification Gaussian process System identification Bayesian Operational modal analysis Latent force model
ISSN:	0888-3270, 1096-1216
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!