Green, P.; Maskell, S. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing 2017, 93, 379-396.
Green, P.; Maskell, S. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing 2017, 93, 379-396.
Green, P.; Maskell, S. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing 2017, 93, 379-396.
Green, P.; Maskell, S. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing 2017, 93, 379-396.
Abstract
In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).
Keywords
big data; parameter estimation; model updating; system identification; sequential Monte Carlo sampler
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
