Particle filter-based Gaussian process optimisation for parameter inference
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Edward Boje and Xiaohua Xia, 2014, 8675-8680 p.Conference paper (Refereed)
We propose a novel method for maximum-likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.
Place, publisher, year, edition, pages
2014. 8675-8680 p.
, World Congress,, ISSN 1474-6670 ; Volume 19, Part 1
Particle filtering/Monte Carlo methods; Bayesian methods; Nonlinear system identification
Control Engineering Signal Processing Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-106750DOI: 10.3182/20140824-6-ZA-1003.00278ISBN: 978-3-902823-62-5OAI: oai:DiVA.org:liu-106750DiVA: diva2:718418
19th IFAC World Congress, Cape Town, South Africa, August 24-29
ProjectsProbabilistic modelling of dynamical systems
FunderSwedish Research Council, 621-2013-5524