Nonlinear model decoupling using a tensor decomposition initialization

Gabriel Hollander; Philippe Dreesen; Mariya Kamenova Ishteva; Joannes Schoukens

Nonlinear model decoupling using a tensor decomposition initialization

Gabriel Hollander, Philippe Dreesen, Mariya Kamenova Ishteva, Joannes Schoukens

Onderzoeksoutput: Unpublished paper

Samenvatting

Finding an approximate model of a given model, by reducing its number of parameters, while keeping the accuracy as good as possible, is of great importance for any existing model. Currently, some nonlinear block-oriented models are hard to interpret, and in need of simplification. For this, we have developed a decoupling method, using tensor decompositions.

Originele taal-2	English
Pagina's	97
Aantal pagina's	1
Status	Published - 28 mrt. 2017
Evenement	36th Benelux Meeting on Systems and Control - Sol-Cress, Spa, Spa, Belgium Duur: 28 mrt. 2017 → 30 mrt. 2017 http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/

Conference

Conference	36th Benelux Meeting on Systems and Control
Land/Regio	Belgium
Stad	Spa
Periode	28/03/17 → 30/03/17
Internet adres	http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/

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author = "Gabriel Hollander and Philippe Dreesen and Ishteva, {Mariya Kamenova} and Joannes Schoukens",

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T1 - Nonlinear model decoupling using a tensor decomposition initialization

AU - Hollander, Gabriel

AU - Dreesen, Philippe

AU - Ishteva, Mariya Kamenova

AU - Schoukens, Joannes

PY - 2017/3/28

Y1 - 2017/3/28

N2 - Finding an approximate model of a given model, by reducing its number of parameters, while keeping the accuracy as good as possible, is of great importance for any existing model. Currently, some nonlinear block-oriented models are hard to interpret, and in need of simplification. For this, we have developed a decoupling method, using tensor decompositions.

AB - Finding an approximate model of a given model, by reducing its number of parameters, while keeping the accuracy as good as possible, is of great importance for any existing model. Currently, some nonlinear block-oriented models are hard to interpret, and in need of simplification. For this, we have developed a decoupling method, using tensor decompositions.

M3 - Unpublished paper

SP - 97

T2 - 36th Benelux Meeting on Systems and Control

Y2 - 28 March 2017 through 30 March 2017

ER -

Nonlinear model decoupling using a tensor decomposition initialization

Samenvatting

Conference

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