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Onderzoeksoutput: Unpublished paper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	32
Aantal pagina's	1
Status	Published - 24 apr. 2017
Evenement	Workshop on Nonlinear System Identification Benchmarks 2017 - Vrije Universiteit Brussels, U-residence, Brussels, Belgium Duur: 24 apr. 2017 → 26 apr. 2017 http://homepages.vub.ac.be/~mschouke/FILES/BenchmarkWorkshop2017_Abstracts.pdf

Conference

Conference	Workshop on Nonlinear System Identification Benchmarks 2017
Land/Regio	Belgium
Stad	Brussels
Periode	24/04/17 → 26/04/17
Internet adres	http://homepages.vub.ac.be/~mschouke/FILES/BenchmarkWorkshop2017_Abstracts.pdf

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abstract = "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.",

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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

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Y1 - 2017/4/24

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 - 32

T2 - Workshop on Nonlinear System Identification Benchmarks 2017

Y2 - 24 April 2017 through 26 April 2017

ER -

Nonlinear model decoupling using a tensor decomposition initialization

Samenvatting

Conference

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