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

doi:10.1016/j.ifacol.2020.12.841

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 journal › Conference article › peer-review

4 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 language	English
Pages (from-to)	845-850
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	53
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.841
State	Published - 2020
Externally published	Yes
Event	21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020

Keywords

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

Access to Document

10.1016/j.ifacol.2020.12.841

Cite this

@article{4bb257cd4120485a9f1fbc31965c1e63,

title = "Model error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems",

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.",

keywords = "Estimation, Expectation-Maximization, Gaussian Mixture, Maximum Likelihood, Model errors, Stochastic Embedding",

author = "Rafael Orellana and Rodrigo Carvajal and Ag{\"u}ero, {Juan C.} and Goodwin, {Graham C.}",

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

year = "2020",

doi = "10.1016/j.ifacol.2020.12.841",

language = "English",

volume = "53",

pages = "845--850",

journal = "IFAC-PapersOnLine",

issn = "1474-6670",

number = "2",

}

TY - JOUR

T1 - Model error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems

AU - Orellana, Rafael

AU - Carvajal, Rodrigo

AU - Agüero, Juan C.

AU - Goodwin, Graham C.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Estimation

KW - Expectation-Maximization

KW - Gaussian Mixture

KW - Maximum Likelihood

KW - Model errors

KW - Stochastic Embedding

UR - http://www.scopus.com/inward/record.url?scp=85106862564&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.841

DO - 10.1016/j.ifacol.2020.12.841

M3 - Conference article

AN - SCOPUS:85106862564

SN - 1474-6670

VL - 53

SP - 845

EP - 850

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this