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 Matem. Mod., 2020, Volume 32, Number 8, Pages 91–105 (Mi mm4207)

Mathematical model of the neural network conclusion defuzzificator in fuzzy-logic output procedures and its software implementation

S. P. Dudarov, N. D. Kirillov

Mendeleev University of Chemical Technology of Russia, Moscow

Abstract: This paper presents a mathematical model of a neural network defuzzificator. It is a twolayer perceptron and serves to convert a fuzzy solution to a numerical form in fuzzy logic derivation procedures. The model allows to optimize the computational load that occurs when using the standard center of gravity method, through the use of a neural network. Training and testing was conducted with various settings of the neural network model. The effectiveness of this approach with measuring the time of computing operations was also proved.

Keywords: neural network defuzzificator, neural network model, neural network, defuzzification, fuzzy-logical derivation, mathematical model of a defuzzificator.

DOI: https://doi.org/10.20948/mm-2020-08-06

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Revised: 14.10.2019
Accepted:25.11.2019

Citation: S. P. Dudarov, N. D. Kirillov, “Mathematical model of the neural network conclusion defuzzificator in fuzzy-logic output procedures and its software implementation”, Matem. Mod., 32:8 (2020), 91–105

Citation in format AMSBIB
\Bibitem{DudKir20} \by S.~P.~Dudarov, N.~D.~Kirillov \paper Mathematical model of the neural network conclusion defuzzificator in fuzzy-logic output procedures and its software implementation \jour Matem. Mod. \yr 2020 \vol 32 \issue 8 \pages 91--105 \mathnet{http://mi.mathnet.ru/mm4207} \crossref{https://doi.org/10.20948/mm-2020-08-06}