A novel approach to model error modelling using the expectation- maximization algorithm

Ramon A. Delgado; Graham C. Goodwin; Rodrigo Carvajal; Juan C. Aguero

doi:10.1109/CDC.2012.6426633

A novel approach to model error modelling using the expectation- maximization algorithm

Ramon A. Delgado, Graham C. Goodwin, Rodrigo Carvajal, Juan C. Aguero

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

15 Scopus citations

Abstract

In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors; (ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding; (iii) we estimate both, the nominal model and undermodelling simultaneously using the Expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.

Original language	English
Article number	6426633
Pages (from-to)	7327-7332
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6426633
State	Published - 2012
Externally published	Yes
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: 10 Dec 2012 → 13 Dec 2012

Keywords

EM algorithm
model error modelling
system identification

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10.1109/CDC.2012.6426633

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title = "A novel approach to model error modelling using the expectation- maximization algorithm",

abstract = "In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors; (ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding; (iii) we estimate both, the nominal model and undermodelling simultaneously using the Expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.",

keywords = "EM algorithm, model error modelling, system identification",

author = "Delgado, {Ramon A.} and Goodwin, {Graham C.} and Rodrigo Carvajal and Aguero, {Juan C.}",

year = "2012",

doi = "10.1109/CDC.2012.6426633",

language = "English",

pages = "7327--7332",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0743-1546",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "51st IEEE Conference on Decision and Control, CDC 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

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AU - Goodwin, Graham C.

AU - Carvajal, Rodrigo

AU - Aguero, Juan C.

PY - 2012

Y1 - 2012

N2 - In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors; (ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding; (iii) we estimate both, the nominal model and undermodelling simultaneously using the Expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.

AB - In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors; (ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding; (iii) we estimate both, the nominal model and undermodelling simultaneously using the Expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.

KW - EM algorithm

KW - model error modelling

KW - system identification

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U2 - 10.1109/CDC.2012.6426633

DO - 10.1109/CDC.2012.6426633

M3 - Conference article

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SN - 0743-1546

SP - 7327

EP - 7332

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

M1 - 6426633

T2 - 51st IEEE Conference on Decision and Control, CDC 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

A novel approach to model error modelling using the expectation- maximization algorithm

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Keywords

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