Green, PL and Maskell, S ORCID: 0000-0003-1917-2913
(2017)
Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers.
Mechanical Systems and Signal Processing, 93.
pp. 379-396.
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Abstract
In this paper the authors present a method which facilitates computationally efficientparameter estimation of dynamical systems from a continuously growing set of measure-ment data. It is shown that the proposed method, which utilises Sequential Monte Carlosamplers, is guaranteed to be fully parallelisable (in contrast to Markov chain MonteCarlo methods) and can be applied to a wide variety of scenarios within structural dynam-ics. Its ability to allowconvergenceof one’s parameter estimates, as more data is analysed,sets it apart from other sequential methods (such as the particle filter).
Item Type: | Article |
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Uncontrolled Keywords: | Big Data, Parameter estimation, Model updating, System identification, Sequential Monte Carlo sampler |
Depositing User: | Symplectic Admin |
Date Deposited: | 18 Sep 2017 08:36 |
Last Modified: | 19 Jan 2023 06:54 |
DOI: | 10.1016/j.ymssp.2016.12.023 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3009426 |
Available Versions of this Item
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Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. (deposited 20 Dec 2016 11:21)
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Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. (deposited 21 Dec 2016 09:37)
- Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. (deposited 18 Sep 2017 08:36) [Currently Displayed]
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Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. (deposited 21 Dec 2016 09:37)