Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers

被引：17
|
作者：
Green, P. L. [1 ,3 ]
Maskell, S. [2 ,3 ]
机构：
[1] Univ Liverpool, Sch Engn, Liverpool L69 7ZF, Merseyside, England
[2] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 7ZF, Merseyside, England
[3] Univ Liverpool, Inst Risk & Uncertainty, Liverpool L69 7ZF, Merseyside, England
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2017年 / 93卷
关键词：
Big Data; Parameter estimation; Model updating; System identification; Sequential Monte Carlo sampler; TRAINING DATA; IDENTIFICATION; MODELS;
D O I：
10.1016/j.ymssp.2016.12.023
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
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). (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：379 / 396
页数：18
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