Recursive identification of time-varying Hammerstein systems with matrix forgetting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F23%3APU147547" target="_blank" >RIV/00216305:26620/23:PU147547 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9815531" target="_blank" >https://ieeexplore.ieee.org/document/9815531</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TAC.2022.3188478" target="_blank" >10.1109/TAC.2022.3188478</a>
Alternative languages
Result language
angličtina
Original language name
Recursive identification of time-varying Hammerstein systems with matrix forgetting
Original language description
The real-time estimation of the time-varying Hammerstein system by using a noniterative learning schema is considered and extended to incorporate a matrix forgetting factor. The estimation is cast in a variational-Bayes framework to best emulate the original posterior distribution of the parameters within the set of distributions with feasible moments. The recursive concept we propose approximates the exact posterior comprising undistorted information about the estimated parameters. In many practical settings, the incomplete model of parameter variations is compensated by forgetting of obsolete information. As a rule, the forgetting operation is initiated by the inclusion of an appropriate prediction alternative into the time update. It is shown that the careful formulation of the prediction alternative, which relies on Bayesian conditioning, results in partial forgetting. This article inspects two options with respect to the order of the conditioning in the posterior, which proves vital in the successful localization of the source of inconsistency in the data-generating process. The geometric mean of the discussed alternatives then modifies recursive learning through the matrix forgetting factor. We adopt the decision-making approach to revisit the posterior uncertainty by dynamically allocating the probability to each of the prediction alternatives to be combined.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN
0018-9286
e-ISSN
1558-2523
Volume of the periodical
68
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
3078-3085
UT code for WoS article
000979661300032
EID of the result in the Scopus database
2-s2.0-85134255954