A Right Coprime Factorization of Neural State Space Models

Jan Dimon Bendtsen

doi:10.1109/ISDA.2007.4389605

A Right Coprime Factorization of Neural State Space Models

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

Abstract

In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice. To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity.

Originalsprog	Engelsk
Titel	Seventh International Conference on Intelligent Systems Design and Applications
Antal sider	7
Forlag	IEEE Computer Society Press
Publikationsdato	2007
Sider	177
ISBN (Trykt)	0-7695-2976-3, 978-0-7695-2976-9
DOI	https://doi.org/10.1109/ISDA.2007.4389605
Status	Udgivet - 2007
Begivenhed	International Conference on Intelligent Systems Design and Applications - Rio de Janeiro, Brasilien Varighed: 20 okt. 2007 → 24 okt. 2007 Konferencens nummer: 7

Konference

Konference	International Conference on Intelligent Systems Design and Applications
Nummer	7
Land/Område	Brasilien
By	Rio de Janeiro
Periode	20/10/2007 → 24/10/2007

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10.1109/ISDA.2007.4389605

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Citationsformater

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abstract = "In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice. To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity. ",

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AB - In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice. To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity.

KW - Youla-Kucera Parametrization

KW - Multilayer perceptrons

KW - State space models

U2 - 10.1109/ISDA.2007.4389605

DO - 10.1109/ISDA.2007.4389605

M3 - Article in proceeding

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BT - Seventh International Conference on Intelligent Systems Design and Applications

PB - IEEE Computer Society Press

T2 - International Conference on Intelligent Systems Design and Applications

Y2 - 20 October 2007 through 24 October 2007

ER -

A Right Coprime Factorization of Neural State Space Models

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