EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models

Angel L. Cedeño; Rafael Orellana; Rodrigo Carvajal; Juan C. Agüero

doi:10.1016/j.ifacol.2020.12.844

EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models

Angel L. Cedeño, Rafael Orellana, Rodrigo Carvajal, Juan C. Agüero

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	863-868
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	53
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.844
State	Published - 2020
Externally published	Yes
Event	21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020

Keywords

Errors-in-variables
Estimation
Expectation-Maximization
Gaussian Mixture
Maximum Likelihood
Optimization

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10.1016/j.ifacol.2020.12.844

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title = "EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models",

abstract = "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.",

keywords = "Errors-in-variables, Estimation, Expectation-Maximization, Gaussian Mixture, Maximum Likelihood, Optimization",

author = "Cede{\~n}o, {Angel L.} and Rafael Orellana and Rodrigo Carvajal and Ag{\"u}ero, {Juan C.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2020 The Authors.; 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

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AU - Orellana, Rafael

AU - Carvajal, Rodrigo

AU - Agüero, Juan C.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Errors-in-variables

KW - Estimation

KW - Expectation-Maximization

KW - Gaussian Mixture

KW - Maximum Likelihood

KW - Optimization

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DO - 10.1016/j.ifacol.2020.12.844

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VL - 53

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IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

EM-based identification of static errors-in-variables systems utilizing Gaussian Mixture models

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Keywords

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