Geodesic
Mathematical model of the neural network conclusion defuzzificator in fuzzy-logic output procedures and its software implementation
Matematičeskoe modelirovanie, Tome 32 (2020) no. 8, pp. 91-105.

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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. 
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S. P. Dudarov; N. D. Kirillov. Mathematical model of the neural network conclusion defuzzificator in fuzzy-logic output procedures and its software implementation. Matematičeskoe modelirovanie, Tome 32 (2020) no. 8, pp. 91-105. http://geodesic.mathdoc.fr/item/MM_2020_32_8_a5/

[1] E. V. Suslova, “Intellektualnye sistemy podderzhki priniatiia reshenii”, Molodoi uchenyi, 2017, no. 3, 171–174

[2] A. A. Baichenko, L. A. Baichenko, V. A. Aret, “Primenenie nechetkoi logiki v upravlenii predpriiatiem pishchevoi promyshlennosti”, Nauch. zhurnal NIU ITMO, 2014, no. 3, 35–69

[3] V. B. Uspenskii, L. V Shipulina, Sovremennaia teoriia upravleniia. Metody sinteza i optimizacii sistem upravleniia, Konspekt lekcii dlia studentov specialnostei 7.080202 i 7.080201, NTUKHPI, Kh., 2013, 136 pp.

[4] I. S. Kobersi, A. V. Kiiashko, E. A. Makedonov, E. R. Kramarenko, V. I. Finaev, “Sistema upravleniia napriazheniem generatora na baze nechetkoi logiki”, Inzhenernyi vestnik Dona, 2015, no. 2-2, 1–14

[5] R. Syahputra, “Application of neuro-fuzzy method for prediction of vehicle fuel consumption”, J. of Theoretical Applied Information Technology, 86:1 (2016), 138–150

[6] R. C. David, R. B. Grad, R. E. Precup, M. B. Rădac, C. A. Dragoş, E. M. Petriu, “An approach to fuzzy modeling of anti-lock braking systems”, Soft Computing in Industrial Applications, Springer, Cham, 223 (2014), 83–93 | DOI

[7] L. Sun, W. Huo, “Adaptive fuzzy control of spacecraft proximity operations using hierarchical fuzzy systems”, IEEE/ASME Trans. Mechatronics, 21:3 (2016), 1629–1640 | DOI

[8] S. P. Dudarov, P. L. Papaev, Algebra nechetkoi logiki i analiz nechetkikh mnozhestv, ucheb. posobie, RKHTU im. D.I. Mendeleeva, M., 2019, 84 pp.

[9] W. T. Dobrosielski, J. Szczepański, H. Zarzycki, “A proposal for a method of defuzzification based on the golden Ratio-GR”, Novel Developments in Uncertainty Representation and Processing, Springer, 2016, 75–84 | DOI

[10] N. S. Ermolaev, N. D. Kirillov, S. P. Dudarov, “Model universalnogo neirosetevogo defazzifikatora”, Uspekhi v khimii i khimicheskoi tekhnologii, 31:8 (189) (2017), 66–68

[11] A. N. Kudriashov, “Modelirovanie rasseianiia primesi v atmosfere s ispolzovaniem nechetkogo kletochnogo avtomata”, Uspekhi v khimii i khim. Tekhnologii, XXVI:1 (130) (2012), 24–28

[12] M. M. Adnan, A. Sarkheyli, A. M. Zain, H. Haron, “Fuzzy logic for modeling machining pro-cess: a review”, Artificial Intelligence Review, 43:3 (2015), 345–379 | DOI

[13] S. P. Dudarov, P. L. Papaev, Teoreticheskie osnovy i prakticheskoe primenenie iskusstvennykh neironnykh setei, Uch. posobie, RKHTU im. D.I. Mendeleeva, M., 2014, 104 pp.

[14] S. O. Ianchi, S. P. Dudarov, “Ispolzovanie obieektno-orientirovannogo podkhoda pri razrabotke programmnykh sredstv neirosetevogo modelirovaniia”, Uspekhi v khimii i khim. tekhnologii, XXII:1(81) (2008), 55–59