NEURO-FUZZY MULTICRITERIA ASSESSMENT MODEL

Authors

  • 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

DOI:

https://doi.org/10.15588/1607-3274-2019-4-8

Keywords:

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

Abstract

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

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Published

2019-11-25

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. https://doi.org/10.15588/1607-3274-2019-4-8

Issue

Section

Neuroinformatics and intelligent systems