Design of data-driven model for the pressurizer system in nuclear power plants using a TSK fuzzy neural network

•Developing a TSKFNN to model the pressurizer unit in PWRs.•Using the fuzzy C means and the kernel ridge regression to initiate and define the TSKFNN structure.•Developing the BP and the kernel RLS to update the TSKFNN model parameters.•Investigate the capability of the TSKNN model using different d...

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Veröffentlicht in:	Nuclear engineering and design Jg. 399; S. 112015
Hauptverfasser:	Mahmoud, Tarek A., Sheta, Amal A., Fikry, Refaat M., Ali, Elsayed H., El-Araby, Sayed M., Mahmoud, Mohammed I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.12.2022 Elsevier BV
Schlagworte:	Algorithms Artificial neural networks Clustering Computer simulation Control systems Data-Driven Modeling Fuzzy logic Kernels Mathematical models Neural networks Nuclear Power Plants Nuclear reactors Parameter identification Pressure Pressurized water reactors Pressurizer System Primary circuits Reactors TSK Fuzzy Neural Network Water levels Pressurizer System Nuclear Power Plants TSK Fuzzy Neural Network Data-Driven Modeling
ISSN:	0029-5493, 1872-759X
Online-Zugang:	Volltext
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