• N. N. Malyar Uzhgorod National University, Uzhgorod
  • A. V. Polishchuk Uzhgorod National University, Uzhgorod
  • V. V. Polishchuk Uzhgorod National University, Uzhgorod
  • M. N. Sharkadi Uzhgorod National University, Uzhgorod



Neuro-fuzzy network, fuzzy knowledge, research object, risk assessment, membership function, peer review, decision making.


Context. The research of the actual problem of development of models and methods of multicriteria evaluation using neurofuzzy
technologies is carried out.
The purpose of this work is to develop a model for obtaining an aggregate evaluation of the significance of the object of study,
which on the one hand uses different characteristics of the object, evaluated by quantitative indicators and on the basis of different
models of representation of knowledge about the object, and on the other uses experience, knowledge and the expertise of experts in
the relevant subject area.
Objective. The object of the study is the process of modeling the experience, knowledge and competence of experts to quantify
the object of study on the basis of neuro-fuzzy networks.
The subject of the study is a neuro-fuzzy model of quantifying an object of study for decision making in expert data.
Method. For the first time, a five-layer neuro-fuzzy model has been developed to derive quantitative and linguistic assessments
of the object of the study using the expertise, expertise and expertise of the subject area. For the first time, it is proposed to use quan-
titative estimates of the object of study (aggregated estimates using multicriteria models) and linguistic expert reasoning on a neurofuzzy
network. For the first time, a model has been tested and verified for an example of assessing the risk of financing a startup
project in the business expansion phase, and is also offered as a training for the neuro-fuzzy synaptic weight interval network. Comparison
of the results of the study on different approaches to determining synaptic weights and real data with error detection.
Results. The result of the study is a neural-fuzzy model for evaluating an object by many criteria. The developed model allows to
combine quantitative characteristics of an object with expert opinions in the form of qualitative estimates. The rationality of the
evaluation proves the advantages of the developed models.
Conclusions. Sharing the apparatus of fuzzy sets and neural networks theory is a convenient simulation tool for multicriteria selection
problems. As a rule, important information for management decision support systems comes from two sources: 1) obtaining
object estimates by certain quantitative indicators, which creates inaccuracy; 2) from expert people who describe their subject matter
knowledge, which creates subjectivity and uncertainty. Therefore, maintaining expert judgment and inaccurate data requires the
ability to work with them. The paper deals with the scientific and applied problem of developing a model for obtaining an aggregate
estimation of an object based on a neural-fuzzy network and can be applied in solving management decision-making problems in
socio-economic systems.

Author Biographies

N. N. Malyar, Uzhgorod National University, Uzhgorod

Doctor of Science, Associate professor, Professor of the Department of Cybernetics and Applied Mathematics

A. V. Polishchuk, Uzhgorod National University, Uzhgorod

PhD Student of the Department of Cybernetics and Applied Mathematics

V. V. Polishchuk, Uzhgorod National University, Uzhgorod

PhD, Associate Professor, Associate Professor of the Department of Software Systems

M. N. Sharkadi, Uzhgorod National University, Uzhgorod

PhD, Associate professor, Associate Professor of the Department of Cybernetics and Applied Mathematics


Wang J. G., Tai S. C., Lin C. J. The application of an interactively recurrent self-evolving fuzzy CMAC classifier on

face detection in color images, Neural Comput. Appl., 2018, 29, pp. 201–213. DOI:

Jhang J.-Y., Tang K.-H., Huang C.-K., Lin C.-J., Young K.-Y. FPGA Implementation of a Functional Neuro-Fuzzy Network

for Nonlinear System Control, Electronics, 2018, 7, P. 145. DOI:

Wu M. F., Huang W. C., Juang C. F., Chang K. M., Wen C. Y., Chen Y.H., Lin C. Y., Chen Y. C., Lin C. C. A new method for

self-estimation of the severity of obstructive sleep apnea using easily available measurements and neural fuzzy evaluation

system, IEEE J. Biomed. Health Inf., 2017, 21, pp. 1524–1532. DOI: 10.1109/JBHI.2016.2633986

Kelemen M., Polishchuk V., Gavurová B., Szabo S., Rozenberg R., Gera M., Kozuba J., Hospodka J., Andoga R., Divoková A.,

Bliš’an P. Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport, Int. J. Environ.

Res. Public Health. 2019, 16, P. 3585. DOI:

Subbotin S. A., Blagodarev A. YU., Gofman Ye. A. Sintez neyro-nechetkikh diagnosticheskikh modeley s kheshiruyushchim preobrazovaniyem v posledovatel’nom i parallel’nom rezhimakh, Radio Electronics, Computer Science, Control, 2017, No. 1, pp. 56–65. DOI:

Oliynyk A. O., Skrups’kyy S. YU., Subbotin S. O., Blahodar’ov A. YU., Hofman YE. O. Planuvannya resursiv paralel’noyi

obchys-lyuval’noyi systemy pry syntezi neyro-nechitkykh modeley dlya obrobky velykykh danykh, Radio Electronics,

Computer Science, Control, 2016, No. 4, pp. 61–69. DOI:

Bodyans’kyy YE. V. Deyneko A. O., Kutsenko YA. V. Poslidovne nechitke klasteruvannya na osnovi neyro-fazzi

pidkhodu, Radio Electronics, Computer Science, Control, 2016, No. 3, pp. 30–38. DOI:


Lin C. J., Chen C. H. Identification and prediction using recurrent compensatory neuro-fuzzy systems, Fuzzy Sets Syst,

, 2004, pp. 307–330. DOI:

Khayat O. Structural parameter tuning of the first-order derivative of an adaptive neuro-fuzzy system for chaotic

function modeling, J. Int. Fuzzy Syst., 2014, 27, pp. 235–245.

Zade L. Ponyatiye lingvisticheskoy peremennoy i yego primeneniye k prinyatiyu priblizhennykh resheniy. Moscow,

Mir, 1976, 167 p.

Rotshteyn O. P. Intelektualni tekhnolohiyi identyfikatsiyi:nechitki mnozhyny, henetychni alhorytmy, neyronni merezhi.

Vinnytsya, UNIVERSUM, 1999, 320 p.

Subbotin S. O. Podannya ta obrobka znan u systemakh shtuchnoho intelektu ta pidtrymky pryynyattya rishen: navch.

posib. Zaporizhzhya, ZNTU, 2008, 341 p. ISBN 978-966-7809-84-4

Oliynyk A. O., Subbotin S. O., Oliynyk O. O. Intelektual’nyy analiz danykh : navchal’nyy posibnyk. Zaporizhzhya, ZNTU,

, 271 p.

Snytyuk V. YE. Prohnozuvannya. Modeli. Metody. Alhorytmy:navch. posib. Kiev, Maklaut, 2008, 364 p.

Zaychenko YU. P. Nechetkiye modeli i metody v intellektualnykh sistemakh: ucheb. Posobiye. Kiev, Slovo,

, 341 p.

Jang R. J.-S., Sun C.-T., Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine

Intelligence. Upper Saddle River, Prentice Hall, 1997.

Sugeno M., Kang G. T. Structure identification of fuzzy model, Fuzzy Sets and Systems, 1998, 28, pp. 15–33.

Bodyanskiy Ye., Zaychenko Yu., Pavlikovskaya E., Samarina M., Viktorov Ye. The neo-fuzzy neural network structure optimization using the GMDH for the solving forecasting and classification problems, Proc. Int. Workshop on Inductive Modeling,

Krynica, Poland, 2009, pp. 77–89.

Malyar М. М., Polishchuk V. V. Nechitki modeli i metody otsinyuvannya kredytospromozhnosti pidpryyemstv ta

investytsiynykh proektiv : monohrafiya. Uzhhorod, RA «AUTDOR-SHARK», 2018, 174 p. ISBN 978-617-7132-85-0.

Polishchuk V., Voloshyn O., Malyar M., Sharkadi M. Fuzzy mathematical modeling financial risks, IEEE Second International

Conference on Data Stream Mining & Processing (DSMP), (Lviv, 21–25 August 2018). Lviv, 2018, pp. 65–69.

DOI: 10.1109/DSMP.2018.8478604

Polishchuk V. Fuzzy Method for Evaluating Commercial Projects of Different Origin, Journal of Automation and Information

Sciences. Begell house, Inc, New York, 2018, Volume 50, Issue 5, pp. 60–73.

Malyar M. M., Polishchuk V. V., Sharkadi M. M. Model informatsiynoyi tekhnolohiyi otsinky ryzyku finansuvannya

proektiv, Radio Electronics, Computer Science, Control, 2017, No. 2, pp. 44–52. DOI: 10.15588/1607-3274-2017-2-5.

Malyar M., Polishchuk V., Sharkadi M., Liakh I. Model of startups assessment under conditions of information uncertainty,

Eastern European Journal of Enterprise Technologies, Mathematics and cybernetics – applied aspects, 2016, No. 3/4

(81), pp. 43–49. DOI: 10.15587/1729-4061.2016.71222.

Polishchuk V. V., Malyar M. M., Polishchuk V. V., Sharkadi M. M. Informatsiyne modelyuvannya nechitkykh znan, Radio

Electronics, Computer Science, Control, 2018, No. 4, pp. 84–95. DOI 10.15588/1607-3274-2018-4-8



How to Cite

Malyar, N. N., Polishchuk, A. V., Polishchuk, V. V., & Sharkadi, M. N. (2019). NEURO-FUZZY MULTICRITERIA ASSESSMENT MODEL. Radio Electronics, Computer Science, Control, (4), 83–91.



Neuroinformatics and intelligent systems