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

Rafael Orellana, Rodrigo Carvajal, Juan C. Agüero, Graham C. Goodwin

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)845-850
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

  • Estimation
  • Expectation-Maximization
  • Gaussian Mixture
  • Maximum Likelihood
  • Model errors
  • Stochastic Embedding

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