Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks

Ido, A. Sharghi; Bonyadi, Mohammad Reza; Etaati, G. R.; Shahriari, M.

doi:10.1016/j.apradiso.2009.05.020

Cited by 33 publications

(17 citation statements)

References 12 publications

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“…8 , in which the difference between the neutron energy spectra (unfolded and ISO) is presented. As shown, the size of the errors for calculations performed using ANFIS is much smaller than the errors reported in the previously published works [ 14 , 19 ]. As displayed in Figs 6 – 8 , the unfolded energy spectra of a neutron sources obtained using ANFIS are more accurate than the results obtained in the previously published paper by Hosseini [ 10 ].…”

Section: Resultscontrasting

confidence: 54%

“…The advantages of both mentioned algorithms are used to improve the effectiveness of the proposed algorithm in different applications such as modelling, control and classification [ 6 – 9 ]. ANFIS is considered to be a universal estimator for complex linear and non-linear systems [ 14 ]. The operation of ANFIS looks like that of a feed-forward back-propagation network.…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Hosseini

Afrakoti

2017

Journal of Radiation Research

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The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi–Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were found to have excellent agreement with the reference data. Also, the unfolded energy spectra of the neutron sources as obtained using ANFIS were more accurate than the results reported from calculations performed using artificial neural networks in previously published papers.

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Section: Resultscontrasting

confidence: 54%

Section: Methodsmentioning

confidence: 99%

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Hosseini

Afrakoti

2017

Journal of Radiation Research

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“…Among researchers, AI technology has been proposed as an alternative approach for solving the drawbacks associated to the neutron spectrum unfolding problem and is gaining popularity since it offers better results compared with the traditional solution methods [43][44][45][46][47].…”

Section: Neutron Spectrometry With Artificial Neural Networkmentioning

confidence: 99%

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

Martínez-Blanco¹,

Serrano-Muñoz²,

Vega-Carrillo³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

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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.

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“…The prior spectrum, time-consuming procedure, the low-resolution spectrum are some drawbacks which these approaches offer [12]. Each of the mentioned difficulties has motivated the development of complementary procedures such as maximum entropy [13], Genetic Algorithms (GA) [14,15] and Artificial Neural Networks (ANN) [16][17][18]. The use of ANN to unfold neutron spectra from the count rates measured with BSS system has become in an alternative procedure [19,20], which has been applied with success, and some authors have expressed that this technology merit attention because of success obtained [21].…”

Section: Introductionmentioning

confidence: 99%

Neural network unfolding of neutron spectrum measured by gold foil-based Bonner sphere

Mohammadi

Hakimabad

Rafat-Motavalli

2014

J Radioanal Nucl Chem

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Bonner sphere spectrometer (BSS) based on gold foils, as thermal neutron detectors, is one of the common device for neutron spectrometry. In this paper, the response functions of the Bonner spheres were determined for neutrons with energies ranging from thermal up to 20 MeV using Monte Carlo code. To verify the simulated response functions, the spheres of BSS were irradiated with 241 Am-Be neutron source at the Ferdowsi University of Mashhad and the activities of the gold foils were measured. Then the artificial neural network was applied for neutron spectrum unfolding. The results showed a good agreement between unfolded and reference spectrum of ISO 8529-1.

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Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks

Cited by 33 publications

References 12 publications

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

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

Neural network unfolding of neutron spectrum measured by gold foil-based Bonner sphere

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