Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely est...

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Detalles Bibliográficos
Autores principales: Karbasi, Masoud, Jamei, Mehdi, Ahmadianfar, Iman, Asadi, Amin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8492736/
https://www.ncbi.nlm.nih.gov/pubmed/34611225
http://dx.doi.org/10.1038/s41598-021-99166-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8492736/
https://www.ncbi.nlm.nih.gov/pubmed/34611225
http://dx.doi.org/10.1038/s41598-021-99166-3

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