Optimality of the higher-order neuron unit approximators with omitted inputs for adaptive control of dynamical systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00379215" target="_blank" >RIV/68407700:21220/24:00379215 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Optimality of the higher-order neuron unit approximators with omitted inputs for adaptive control of dynamical systems
Original language description
This paper evaluates the performance of Higher Order Neuron Unit (HONU) with omitted inputs-based approximators for dynamical systems, focusing on Quadratic Neuron Units (QNUs) and Cubic Neuron Units (CNUs). Despite the reduced feature vector, HONU-based models demonstrate robust approximation capabilities. The optimality, stability, and convergence of the QNU and CNU-based approximators are analyzed and compared. These findings highlight the potential of HONUs for data-driven dynamical system modeling in adaptive control applications.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů