- 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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