Variational system identification for nonlinear state-space models

Title: Variational system identification for nonlinear state-space models
Creator: Courts, Jarrad; Wills, Adrian G.; Schön, Thomas B.; Ninness, Brett
Relation: Automatica Vol. 147, Issue January 2023, no. 110687
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2022.110687
Publisher: Elsevier
Resource Type: journal article
Date: 2023
Description: This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connections to maximum likelihood estimation. This VI approach ultimately provides estimates of the model as solutions to an optimisation problem, which is deterministic, tractable and can be solved using standard optimisation tools. A specialisation of this approach for systems with additive Gaussian noise is also detailed. The proposed method is examined numerically on a range of simulated and real examples focusing on the robustness to parameter initialisation; additionally, favourable comparisons are performed against state-of-the-art alternatives.
Subject: system identification; variational inference; nonlinear models; parameter estimation; assumed density
Identifier: http://hdl.handle.net/1959.13/1478573
Identifier: uon:50195
Identifier: ISSN:0005-1098
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format