EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models Article Swipe
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Angel L. Cedeño
,
Rafael Orellana
,
Rodrigo Carvajal
,
Juan C. Agüero
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.844
· OA: W3153704559
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.844
· OA: W3153704559
In this paper we address the problem of identifying a static errors-in-variables system. Our proposal is based on the Expectation-Maximization algorithm, in which we consider that the distribution of the noise-free input is approximated by a finite Gaussian mixture. This approach allows us to estimate the static system parameters, the input and output noise variances, and the Gaussian mixture parameters. We show the benefits of our proposal via numerical simulations.
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