Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

Martinez-Blanco, Ma. del Rosario and Serrano-Muñoz, Arturo and Vega-Carrillo, Hector Hector Rene and de Sousa-Lacerda, Marco Aurelio and Mendez-Villafañe, Roberto and Gallego, Eduardo and de Santiago, Antonio del Río and Solis-Sanchez, Luis Octavio and Ortiz-Rodriguez, Jose Manuel (2020) Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 7 (24). p. 166667. ISSN 2410-0218

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Abstract

In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.

Item Type: Article
Uncontrolled Keywords: Neutron spectrometry and dosimetry, Artificial intelligence, Generalized regression artificial neural networks, unfolding code, programming
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 21 Jan 2021 06:55
Last Modified: 21 Jan 2021 06:55
URI: https://eprints.eudl.eu/id/eprint/820

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