Application of Extreme Learning Machines to inverse neutron kinetics

Paolo Picca; Roberto Furfaro

doi:10.1016/j.anucene.2016.08.031

Application of Extreme Learning Machines to inverse neutron kinetics

Paolo Picca
, Roberto Furfaro

Systems and Industrial Engineering

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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).

Original language	English (US)
Pages (from-to)	1-8
Number of pages	8
Journal	Annals of Nuclear Energy
Volume	100
DOIs	https://doi.org/10.1016/j.anucene.2016.08.031
State	Published - Feb 1 2017

Keywords

Accelerator-driven system
Artificial Neural Network
Extreme Learning Machines
Inverse neutron kinetics

ASJC Scopus subject areas

Nuclear Energy and Engineering

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10.1016/j.anucene.2016.08.031

Cite this

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title = "Application of Extreme Learning Machines to inverse neutron kinetics",

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).",

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AU - Picca, Paolo

AU - Furfaro, Roberto

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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

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JO - Annals of Nuclear Energy

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ER -

Application of Extreme Learning Machines to inverse neutron kinetics

Abstract

Keywords

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