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.
Highlights
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
Previous researches indicate that Feed Forward Backpropagation Neural Networks (FFBPNN) perform well and this kind of network has been the most popular approach used in this research area [48,49,50,51,52,53,54]
The biggest advantage is the fact that the probabilistic approach of Generalized Regression Artificial Neural Networks (GRANN) works with one-steponly learning and uses a single common radial basis function kernel bandwidth, σ, that is tuned to achieve an optimal Artificial Neural Networks (ANNs) learning [59,60,61]
Summary
The term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. 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. "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. 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. The principles of learning can be applied to machines to improve their performance, and is one of the newest fields in research, innovation and technological development known as Artificial Intelligence (AI) [4, 5], which is part of the new information and communication technologies of the fourth industrial revolution known as Industry 4.0. AI seeks to understand intelligent entities and must be able to store knowledge, to apply the stored knowledge in problem solving and to acquire new knowledge through experience [1]
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