{"id":7269,"date":"2021-04-19T10:00:30","date_gmt":"2021-04-19T07:00:30","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=7269"},"modified":"2021-08-05T13:04:13","modified_gmt":"2021-08-05T10:04:13","slug":"%d0%b2%d0%b8%d0%ba%d0%be%d1%80%d0%b8%d1%81%d1%82%d0%b0%d0%bd%d0%bd%d1%8f-%d0%b0%d0%bf%d0%b0%d1%80%d0%b0%d1%82%d1%96%d0%b2-%d1%88%d1%82%d1%83%d1%87%d0%bd%d0%be%d0%b3%d0%be-%d1%96%d0%bd%d1%82%d0%b5","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=7269","title":{"rendered":"\u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0430\u043f\u0430\u0440\u0430\u0442\u0456\u0432 \u0448\u0442\u0443\u0447\u043d\u043e\u0433\u043e \u0456\u043d\u0442\u0435\u043b\u0435\u043a\u0442\u0443 \u0434\u043b\u044f \u0444\u043e\u0440\u043c\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0444\u0456\u043d\u0430\u043d\u0441\u043e\u0432\u0438\u0445 \u043e\u0431’\u0454\u043a\u0442\u0456\u0432 \u043f\u0440\u0438 \u043f\u043e\u0431\u0443\u0434\u043e\u0432\u0456 \u0421\u041f\u041f\u0420"},"content":{"rendered":"

<\/p>\n

\u0412\u0418\u041a\u041e\u0420\u0418\u0421\u0422\u0410\u041d\u041d\u042f \u0410\u041f\u0410\u0420\u0410\u0422\u0406\u0412 \u0428\u0422\u0423\u0427\u041d\u041e\u0413\u041e \u0406\u041d\u0422\u0415\u041b\u0415\u041a\u0422\u0423 \u0414\u041b\u042f \u0424\u041e\u0420\u041c\u0410\u041b\u0406\u0417\u0410\u0426\u0406\u0407 \u0424\u0406\u041d\u0410\u041d\u0421\u041e\u0412\u0418\u0425 \u041e\u0411’\u0404\u041a\u0422\u0406\u0412 \u041f\u0420\u0418 \u041f\u041e\u0411\u0423\u0414\u041e\u0412\u0406 \u0421\u041f\u041f\u0420<\/p>\n

USING OF ARTIFICIAL INTELLIGENCE DEVICES FOR FINANCIAL OBJECTS FORMALIZATION IN THE DSS CONSTRUCTION<\/p>\n

\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 45-51. \u041d\u043e\u043c\u0435\u0440: \u21161, 2021 (293)<\/strong> \"\"<\/a>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u041e.\u0412. \u0420\u0423\u0417\u0410\u041a\u041e\u0412\u0410, \u041d.\u041f. \u042e\u0420\u0427\u0423\u041a
\n\u0412\u0456\u043d\u043d\u0438\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0430\u0433\u0440\u0430\u0440\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\nO. RUZAKOVA, N.YURCHUK
\nVinnytsia National Agrarian University
\nDOI:<\/strong> https:\/\/www.doi.org\/10.31891\/2307-5732-2021-293-1-45-51<\/a>
\n\u0420\u0435\u0446\u0435\u043d\u0437\u0456\u044f\/Peer review :<\/strong> 14.2.2021 \u0440.
\n\u041d\u0430\u0434\u0440\u0443\u043a\u043e\u0432\u0430\u043d\u0430\/Printed :<\/strong> 10.03.2021 \u0440.<\/p>\n

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\n\u0412\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u043e, \u0449\u043e \u043e\u043a\u0440\u0456\u043c \u0442\u0440\u0430\u0434\u0438\u0446\u0456\u0439\u043d\u0438\u0445 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u0434\u043b\u044f \u0444\u043e\u0440\u043c\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0441\u0438\u0441\u0442\u0435\u043c\u0438 \u043f\u0456\u0434\u0442\u0440\u0438\u043c\u043a\u0438 \u043f\u0440\u0438\u0439\u043d\u044f\u0442\u0442\u044f \u0440\u0456\u0448\u0435\u043d\u044c \u0443 \u0444\u0456\u043d\u0430\u043d\u0441\u043e\u0432\u043e\u043c\u0443 \u0430\u043d\u0430\u043b\u0456\u0437\u0456 \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u044e\u0442\u044c\u0441\u044f \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0456 \u043c\u0435\u0440\u0435\u0436\u0456. \u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u043d\u043e \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443 \u043c\u0435\u0440\u0435\u0436\u0443 \u043c\u043e\u0436\u043d\u0430 \u0440\u043e\u0437\u0433\u043b\u044f\u0434\u0430\u0442\u0438 \u044f\u043a \u043a\u043b\u0430\u0441 \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u043e\u0433\u043e \u043c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f, \u0449\u043e \u0443 \u0441\u0432\u043e\u044e \u0447\u0435\u0440\u0433\u0443 \u043c\u043e\u0436\u043d\u0430 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 \u043d\u0430 \u0442\u0440\u0438 \u043a\u043b\u0430\u0441\u0438: \u043e\u0446\u0456\u043d\u043a\u0430 \u0449\u0456\u043b\u044c\u043d\u043e\u0441\u0442\u0456 \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u043e\u0441\u0442\u0456, \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044f \u0456 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f.
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\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

The work considers the financial objects formalization in the DSS construction based on artificial intelligence devices. It is determined that there is a need to choose the optimal mathematical apparatus, taking into account the specifics of solving a particular financial problem for the implementation of decision-making procedures. It is noted that the emergence of full-fledged statistics will return to the use of probabilities in risk analysis and at the same time improve the fuzzy classification quality of enterprise financial parameters. The formation of input \/ output parameters sets of decision support systems for assessing the enterprise financial condition is considered. Appropriate knowledge matrices have been compiled to assess the quantitative and qualitative characteristics of the enterprises financial condition.
\nIt is established that in addition to traditional statistical methods neural networks are used in financial analysis to formalize the decision support system. Mathematically, a neural network can be considered as a class of statistical modeling methods, which in turn can be divided into three classes: probability density estimation, classification and regression.
\nIt is proved that DSS can be fully implemented on a neural network. In contrast to the traditional use of such NN to solve only the problems of pattern recognition and formation, in DSS on the basis of NN coordinated tasks are solved: pattern recognition and formation; obtaining and storing knowledge (empirically found regular connections of images and influences on the object of control); evaluation of qualitative characteristics of images; decision-making.
\nIt is substantiated that the use of fuzzy sets, neural networks and genetic algorithms in the construction of decision support systems is a promising direction in the DSS development.
\nKeywords:<\/strong> decision support systems, artificial intelligence, mathematical model, fuzzy sets, neural network, genetic algorithms, financial condition of the enterprise.<\/p>\n

References<\/strong><\/p>\n

    \n
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