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Artificial Intelligence and Decision Making, 2019, Issue 2, Pages 91–97
DOI: https://doi.org/10.14357/20718594190209 (Mi iipr173) 		 
Evolutionary computation and soft computing

Construction of hybrid neural networks using fuzzy logic elements

N. V. Gridinaab, I. A. Evdokimova, V. I. Solodovnikova

a Center of Information Technologies in Design, Russian Academy of Sciences, Odintsovo, Moscow Regions, Russia
b I. M. Sechenov First Moscow State Medical University, Moscow, Russia
Full-text PDF (579 kB)
DOI: https://doi.org/10.14357/20718594190209
Abstract: The issues of using fuzzy logic elements in the construction and training of hybrid neural networks for proceeding under conditions of uncertainty are considered. An embodiment of the fuzzy relations implementation and the fuzzy compositional output algorithm based on the neural network approach are presented. A neural network model of a fuzzy system is presented as a universal approximator, as well as the case while fuzziness appears at the learning stage.
Keywords: hybrid neural network; fuzzy logic; fuzzy compositional neural networks.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 0071-2019-0001
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: N. V. Gridina, I. A. Evdokimov, V. I. Solodovnikov, “Construction of hybrid neural networks using fuzzy logic elements”, Artificial Intelligence and Decision Making, 2019, no. 2, 91–97
Citation in format AMSBIB
\Bibitem{GriEvdSol19}
\by N.~V.~Gridina, I.~A.~Evdokimov, V.~I.~Solodovnikov
\paper Construction of hybrid neural networks using fuzzy logic elements
\jour Artificial Intelligence and Decision Making
\yr 2019
\issue 2
\pages 91--97
\mathnet{http://mi.mathnet.ru/iipr173}
\crossref{https://doi.org/10.14357/20718594190209}
\elib{https://elibrary.ru/item.asp?id=38303579}
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