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- W2551629713 abstract "Abstract The paper presents the application of Extreme Leaning Machines (ELMs) for inverse reactor kinetic applications. ELMs were proposed by Huang and co-workers (2004, 2006a,b, 2015), which showed their enhances capabilities in terms of training speed and generalization with respect to classical Artificial Neural Networks (ANNs). ELMs are here implemented for reactivity determination as an alternative to ANNs (e.g. Picca et al. (2008)) and Gaussian Processes (Picca and Furfaro, 2012). After a review of the main features of ELMs, their application to inverse kinetic problems is proposed. The ELMs performance is tested on a typical accelerator drive system configuration (Yalina reactor) and the inversion is carried out on an accurate kinetic model (multi-group transport)." @default.
- W2551629713 created "2016-11-30" @default.
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- W2551629713 date "2017-02-01" @default.
- W2551629713 modified "2023-09-26" @default.
- W2551629713 title "Application of Extreme Learning Machines to inverse neutron kinetics" @default.
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- W2551629713 doi "https://doi.org/10.1016/j.anucene.2016.08.031" @default.
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