Model reference multiple-degree-of-freedom adaptive control with HONUs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00305514" target="_blank" >RIV/68407700:21220/16:00305514 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7727843/" target="_blank" >http://ieeexplore.ieee.org/document/7727843/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727843" target="_blank" >10.1109/IJCNN.2016.7727843</a>
Alternative languages
Result language
angličtina
Original language name
Model reference multiple-degree-of-freedom adaptive control with HONUs
Original language description
This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors. Furthermore, the control scheme is enhanced because potentially occurring disturbances of controlled variable can result in that the sample-by-sample adapted controllers may tend to adaptively force the plant output to merely follow the reference model output because input data are affected by the error of disturbance, while the overall control loop dynamics would be more accurately adapted if no perturbation occurs or if the reference model is actuated with actually measured variables. Furthermore, the controller weight updates usually involve some step-delayed computations that might be in fact recalculated with the latest updated weights, so the controller learning can be improved.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of International Joint Conference on Neural Networks 2016
ISBN
9781509006199
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
4895-4900
Publisher name
IEEE
Place of publication
New York
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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