Model error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems

Title: Model error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems
Creator: Orellana, Rafael; Carvajal, Rodrigo; Agüero, Juan C.; Goodwin, Graham C.
Relation: 21st IFAC World Congress. Proceedings of the 21st IFAC World Congress [presented in IFAC-PapersOnLine, Vol. 53 No.2] (Berlin, Germany 11-17 July, 2020) p. 845-850
Publisher Link: http://dx.doi.org/10.1016/j.ifacol.2020.12.841
Publisher: Elsevier
Resource Type: conference paper
Date: 2020
Description: In this paper a Maximum Likelihood estimation algorithm for error-model modelling using a stochastic embedding approach is developed. The error-model distribution is approximated by a finite Gaussian mixture. An Expectation-Maximization based algorithm is proposed to estimate the nominal model and the distribution of the parameters of the error-model by using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.
Subject: model errors; stochastic embedding; maximum likelihood; Gaussian mixture; expectation-maximization; estimation
Identifier: http://hdl.handle.net/1959.13/1439399
Identifier: uon:40911
Identifier: ISSN:2405-8963
Language: eng
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