Recursive identification of the Hammerstein model based on the Variational Bayes method
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Recursive identification of the Hammerstein model based on the Variational Bayes method
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/GA19-23815S" target="_blank" >GA19-23815S: Identification of Nonlinear Fractional-Order Dynamical Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
60th IEEE Conference on Decision and Control
ISBN
978-1-6654-3659-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1586-1591
Publisher name
IEEE
Place of publication
Austin, Texas, USA
Event location
Austin, Texas, USA
Event date
Dec 13, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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