Recursive identification of the Hammerstein model based on the Variational Bayes method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU142598" target="_blank" >RIV/00216305:26620/21:PU142598 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/9682878" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9682878</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC45484.2021.9682878" target="_blank" >10.1109/CDC45484.2021.9682878</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recursive identification of the Hammerstein model based on the Variational Bayes method

Popis výsledku v původním jazyce

The estimation of the Hammerstein system by using a noniterative learning schema is considered, and a novel algorithm based on the Variational Bayes method is presented. To best emulate the original distribution of the system parameters within the set of those with feasible moments, the loss functional is constructed to optimally approximate the true distribution by a product of independent marginals. To guarantee the uniqueness of the model parameterization, the hard equality constraint is imposed on the selected parameter mean value. In our adopted recursive scenario, the transmission of the approximated moments via iterative cycles is avoided by propagating the sufficient statistics associated with the overparameterized model, which is linear in unknown parameters. Moreover, this propagation penalizes the difference of the updated parameters from the previous ones rather than from the initial guess. Due to access to the sufficient statistics and the suitably chosen marginals, the solution we propose is produced in closed form.

Název v anglickém jazyce

Recursive identification of the Hammerstein model based on the Variational Bayes method

Popis výsledku anglicky

The estimation of the Hammerstein system by using a noniterative learning schema is considered, and a novel algorithm based on the Variational Bayes method is presented. To best emulate the original distribution of the system parameters within the set of those with feasible moments, the loss functional is constructed to optimally approximate the true distribution by a product of independent marginals. To guarantee the uniqueness of the model parameterization, the hard equality constraint is imposed on the selected parameter mean value. In our adopted recursive scenario, the transmission of the approximated moments via iterative cycles is avoided by propagating the sufficient statistics associated with the overparameterized model, which is linear in unknown parameters. Moreover, this propagation penalizes the difference of the updated parameters from the previous ones rather than from the initial guess. Due to access to the sufficient statistics and the suitably chosen marginals, the solution we propose is produced in closed form.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GA19-23815S" target="_blank" >GA19-23815S: Identifikace nelineárních dynamických systémů zlomkového řádu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

60th IEEE Conference on Decision and Control

ISBN

978-1-6654-3659-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1586-1591

Název nakladatele

IEEE

Místo vydání

Austin, Texas, USA

Místo konání akce

Austin, Texas, USA

Datum konání akce

13. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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