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 journalConference articlepeer-review

3 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 languageEnglish
Pages (from-to)863-868
Number of pages6
JournalIFAC-PapersOnLine
Volume53
Issue number2
DOIs
StatePublished - 2020
Externally publishedYes
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020

Keywords

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

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