| Title of paper:
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Intuitionistic fuzzy evaluation of artificial neural network model
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| Author(s):
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| Todor Petkov
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| “Prof. Dr. Assen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
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| todor_petkov@btu.bg
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| Veselina Bureva
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| “Prof. Dr. Assen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
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| vbureva@btu.bg
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| Stanislav Popov
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| “Prof. Dr. Assen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
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| stani_popov@yahoo.com
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 27 (2021), Number 4, pages 71-77
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| DOI:
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https://doi.org/10.7546/nifs.2021.27.4.71-77
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| Download:
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 PDF (1251 Kb, Info)
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| Abstract:
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In this paper a method that evaluates a trained artificial neural network is presented. The learning type of an artificial neural network is supervised learning which requires labeled input training vectors. Labeled medical data is provided to train the network, where the labels can either be 1 if the person is alive, or 0 if the person has deceased. The data is divided into training and validation vectors. The validation input vectors are used to evaluate the model and the results are summarized by using intuitionistic fuzzy values.
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| Keywords:
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Intuitionistic fuzzy evaluation, Intuitionistic fuzzy sets, Neural network.
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| AMS Classification:
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03E72.
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| References:
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| Citations:
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