TY - JOUR
T1 - Application of Extreme Learning Machines to inverse neutron kinetics
AU - Picca, Paolo
AU - Furfaro, Roberto
N1 - Publisher Copyright:
© 2016
PY - 2017/2/1
Y1 - 2017/2/1
N2 - 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).
AB - 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).
KW - Accelerator-driven system
KW - Artificial Neural Network
KW - Extreme Learning Machines
KW - Inverse neutron kinetics
UR - http://www.scopus.com/inward/record.url?scp=84994593667&partnerID=8YFLogxK
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U2 - 10.1016/j.anucene.2016.08.031
DO - 10.1016/j.anucene.2016.08.031
M3 - Article
AN - SCOPUS:84994593667
SN - 0306-4549
VL - 100
SP - 1
EP - 8
JO - Annals of Nuclear Energy
JF - Annals of Nuclear Energy
ER -