@article{Malyar_Polishchuk_Polishchuk_Sharkadi_2019, title={NEURO-FUZZY MULTICRITERIA ASSESSMENT MODEL}, url={http://ric.zntu.edu.ua/article/view/193194}, DOI={10.15588/1607-3274-2019-4-8}, abstractNote={Context. The research of the actual problem of development of models and methods of multicriteria evaluation using neurofuzzy<br />technologies is carried out.<br />The purpose of this work is to develop a model for obtaining an aggregate evaluation of the significance of the object of study,<br />which on the one hand uses different characteristics of the object, evaluated by quantitative indicators and on the basis of different<br />models of representation of knowledge about the object, and on the other uses experience, knowledge and the expertise of experts in<br />the relevant subject area.<br />Objective. The object of the study is the process of modeling the experience, knowledge and competence of experts to quantify<br />the object of study on the basis of neuro-fuzzy networks.<br />The subject of the study is a neuro-fuzzy model of quantifying an object of study for decision making in expert data.<br />Method. For the first time, a five-layer neuro-fuzzy model has been developed to derive quantitative and linguistic assessments<br />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-<br />titative estimates of the object of study (aggregated estimates using multicriteria models) and linguistic expert reasoning on a neurofuzzy<br />network. For the first time, a model has been tested and verified for an example of assessing the risk of financing a startup<br />project in the business expansion phase, and is also offered as a training for the neuro-fuzzy synaptic weight interval network. Comparison<br />of the results of the study on different approaches to determining synaptic weights and real data with error detection.<br />Results. The result of the study is a neural-fuzzy model for evaluating an object by many criteria. The developed model allows to<br />combine quantitative characteristics of an object with expert opinions in the form of qualitative estimates. The rationality of the<br />evaluation proves the advantages of the developed models.<br />Conclusions. Sharing the apparatus of fuzzy sets and neural networks theory is a convenient simulation tool for multicriteria selection<br />problems. As a rule, important information for management decision support systems comes from two sources: 1) obtaining<br />object estimates by certain quantitative indicators, which creates inaccuracy; 2) from expert people who describe their subject matter<br />knowledge, which creates subjectivity and uncertainty. Therefore, maintaining expert judgment and inaccurate data requires the<br />ability to work with them. The paper deals with the scientific and applied problem of developing a model for obtaining an aggregate<br />estimation of an object based on a neural-fuzzy network and can be applied in solving management decision-making problems in<br />socio-economic systems.}, number={4}, journal={Radio Electronics, Computer Science, Control}, author={Malyar, N. N. and Polishchuk, A. V. and Polishchuk, V. V. and Sharkadi, M. N.}, year={2019}, month={Nov.}, pages={83–91} }