{"id":13414,"date":"2022-08-21T11:11:04","date_gmt":"2022-08-21T08:11:04","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=13414"},"modified":"2022-10-03T14:52:05","modified_gmt":"2022-10-03T11:52:05","slug":"meta-model-proektuvannya-prototypu-informaczijnoyi-tehnologiyi-klasyfikacziyi-obyektiv-za-formoyu-zobrazhennya-iz-zastosuvannyam-mgua-nejronnyh-merezh-v-notacziyi-unifikovanoyi-movy-modelyuva","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=13414","title":{"rendered":"\u041c\u0435\u0442\u0430-\u041c\u043e\u0434\u0435\u043b\u044c \u043f\u0440\u043e\u0435\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u043f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u0443 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0439\u043d\u043e\u0457 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457 \u043e\u0431\u2019\u0454\u043a\u0442\u0456\u0432 \u0437\u0430 \u0444\u043e\u0440\u043c\u043e\u044e \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u043d\u044f \u0456\u0437 \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u041c\u0413\u0423\u0410 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436 \u0432 \u043d\u043e\u0442\u0430\u0446\u0456\u0457 \u0443\u043d\u0456\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u043e\u0457 \u043c\u043e\u0432\u0438 \u043c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f"},"content":{"rendered":"

 <\/p>\n

<\/p>\n

\u041c\u0415\u0422\u0410-\u041c\u041e\u0414\u0415\u041b\u042c \u041f\u0420\u041e\u0415\u041a\u0422\u0423\u0412\u0410\u041d\u041d\u042f \u041f\u0420\u041e\u0422\u041e\u0422\u0418\u041f\u0423 \u0406\u041d\u0424\u041e\u0420\u041c\u0410\u0426\u0406\u0419\u041d\u041e\u0407 \u0422\u0415\u0425\u041d\u041e\u041b\u041e\u0413\u0406\u0407 \u041a\u041b\u0410\u0421\u0418\u0424\u0406\u041a\u0410\u0426\u0406\u0407 \u041e\u0411\u2019\u0404\u041a\u0422\u0406\u0412 \u0417\u0410 \u0424\u041e\u0420\u041c\u041e\u042e \u0417\u041e\u0411\u0420\u0410\u0416\u0415\u041d\u041d\u042f \u0406\u0417 \u0417\u0410\u0421\u0422\u041e\u0421\u0423\u0412\u0410\u041d\u041d\u042f\u041c \u041c\u0413\u0423\u0410 \u041d\u0415\u0419\u0420\u041e\u041d\u041d\u0418\u0425 \u041c\u0415\u0420\u0415\u0416 \u0412 \u041d\u041e\u0422\u0410\u0426\u0406\u0407 \u0423\u041d\u0406\u0424\u0406\u041a\u041e\u0412\u0410\u041d\u041e\u0407 \u041c\u041e\u0412\u0418 \u041c\u041e\u0414\u0415\u041b\u042e\u0412\u0410\u041d\u041d\u042f<\/p>\n

META-MODEL OF DESIGN OF INFORMATION TECHNOLOGY PROTOTYPE OF CLASSIFICATION OF OBJECTS BY IMAGE SHAPE USING GMDH NEURAL NETWORKS IN THE NOTATION OF A UNIFIED MODELING LANGUAGE<\/p>\n

\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438:\u00a078-81. \u041d\u043e\u043c\u0435\u0440: \u21164, 2022 (311)\u00a0\u00a0 \"\"<\/a> <\/strong>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u0414\u0410\u0412\u0418\u0414\u0415\u041d\u041a\u041e \u041d. \u0412.
\n\u041b\u0443\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\nhttps:\/\/orcid.org\/0000-0002-9722-745X<\/a>
\ne-mail: n.v.davydenko@lntu.edu.ua<\/a>
\nNina DAVYDENKO
\nLutsk National Technical University
\nDOI:<\/strong>\u00a0https:\/\/www.doi.org\/10.31891\/2307-5732-2022-311-4-78-81<\/a><\/p>\n

\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n

\u0412 \u0440\u043e\u0431\u043e\u0442\u0456 \u0437\u0430\u043f\u0440\u043e\u043f\u043e\u043d\u043e\u0432\u0430\u043d\u043e \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0438 \u043e\u0431\u2019\u0454\u043a\u0442\u043d\u043e-\u043e\u0440\u0456\u0454\u043d\u0442\u043e\u0432\u0430\u043d\u043e\u0457 \u0444\u043e\u0440\u043c\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u043f\u0440\u043e\u0446\u0435\u0441\u0443 \u043f\u0440\u043e\u0454\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0439\u043d\u043e\u0457 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457 \u043e\u0431\u2019\u0454\u043a\u0442\u0456\u0432, \u044f\u043a\u0430 \u0431\u0430\u0437\u0443\u0454\u0442\u044c\u0441\u044f \u043d\u0430 \u0432\u0438\u0431\u043e\u0440\u0456 \u043a\u0440\u0430\u0449\u043e\u0457 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0438 \u043c\u043e\u0434\u0435\u043b\u0456 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0442\u043e\u0440\u0430 \u0437 \u043d\u0430\u0431\u043e\u0440\u0443 \u043c\u043e\u0434\u0435\u043b\u0435\u0439-\u043a\u0430\u043d\u0434\u0438\u0434\u0430\u0442\u0456\u0432, \u0437\u0434\u0430\u0442\u043d\u0438\u0445 \u0434\u043e \u0441\u0430\u043c\u043e\u043e\u0440\u0433\u0430\u043d\u0456\u0437\u0430\u0446\u0456\u0457. \u0412\u0438\u0440\u0456\u0448\u0430\u043b\u044c\u043d\u0435 \u043f\u0440\u0430\u0432\u0438\u043b\u043e \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457 \u0431\u0443\u0434\u0443\u0454\u0442\u044c\u0441\u044f \u0432 \u0431\u043b\u043e\u0446\u0456 \u0441\u0438\u043d\u0442\u0435\u0437\u0443 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0439\u043d\u043e\u0457 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457. \u042f\u043a \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0441\u0438\u043d\u0442\u0435\u0437\u0443 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0430\u043d\u043e \u041c\u0413\u0423\u0410 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0456 \u043c\u0435\u0440\u0435\u0436\u0456. \u0412\u0438\u0431\u0456\u0440 \u043a\u0440\u0430\u0449\u043e\u0457 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0438 \u043c\u043e\u0434\u0435\u043b\u0456 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f \u0437\u0430 \u043d\u0430\u0431\u043e\u0440\u043e\u043c \u043a\u0440\u0438\u0442\u0435\u0440\u0456\u0457\u0432. \u041f\u043e\u0431\u0443\u0434\u043e\u0432\u0443 \u043c\u0435\u0442\u0430-\u043c\u043e\u0434\u0435\u043b\u0456 \u043f\u0440\u043e\u0446\u0435\u0441\u0443 \u043f\u0440\u043e\u0454\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043e \u0456\u0437 \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0443\u043d\u0456\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u043e\u0457 \u043c\u043e\u0432\u0438 \u043c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f. \u0424\u0443\u043d\u043a\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0430 \u043c\u0435\u0442\u0430-\u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0430 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u043e\u044e \u0432\u0430\u0440\u0456\u0430\u043d\u0442\u0456\u0432 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f.
\n\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0439\u043d\u0430 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u044f, \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044f, \u043c\u0435\u0442\u0430-\u043c\u043e\u0434\u0435\u043b\u044c, UML-\u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0438.<\/p>\n

\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 \u00a0\u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

The article is devoted to the development of intelligent monitoring systems of technological processes based on the use of machine vision systems. The principles of object-oriented formalization of the process of designing information technology of the classification of objects by the geometric shape of the image obtained from the machine vision system are proposed in the article. The proposed information technology is based on the use of machine learning technologies and provides for the selection of the best structure of the classifier model from a set of candidate models. Construction of candidate models is based on the use of the group method of data handling, based on the principles of self-organization of models. The outline of the image of the object obtained from the intelligent monitoring system is the input information. The set of input data contains a set of morphometric parameters that describe the geometric shape of the figure formed by the contour of the image of the object, as well as the label of the class to which the object belongs. The formation of the set of input data is implemented in the block \u00abImage Processing\u00bb. The decisive rule of classification is built in the block of synthesis of models of information technology of classification. GMDH neural networks were used as an algorithm for model synthesis. The choice of the best structure of the model is performed by a set of criteria. The information technology for constructing the classifier model is implemented by supplementing the block of algorithms for synthesis of MSUA models with the block \u00abClass of models\u00bb, which implements the process of selecting the class of functions for building models, and the block \u00abVerification of models\u00bb, which implements the best model structure. Construction of the meta-model of the design process was performed using a unified modeling language. The functional meta-model is represented by a use case diagram.
\nKeywords:\u00a0<\/strong> information technology, classification, meta-model, UML-diagrams.<\/p>\n

\u041b\u0456\u0442\u0435\u0440\u0430\u0442\u0443\u0440\u0430<\/strong><\/p>\n

    \n
  1. \u041a\u0440\u0443\u043b\u0456\u043a\u043e\u0432\u0441\u044c\u043a\u0438\u0439 \u0411. \u0422\u0435\u043e\u0440\u0435\u0442\u0438\u0447\u043d\u0456 \u043e\u0441\u043d\u043e\u0432\u0438 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0431\u0430\u0433\u0430\u0442\u043e\u043c\u0456\u0440\u043d\u0438\u0445 \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \u0443 \u0445\u0435\u043c\u043c\u0456\u043d\u0433\u043e\u0432\u043e\u043c\u0443 \u043f\u0440\u043e\u0441\u0442\u043e\u0440\u0456 \/ \u0411. \u041a\u0440\u0443\u043b\u0456\u043a\u043e\u0432\u0441\u044c\u043a\u0438\u0439, A. \u0421\u0438\u0434\u043e\u0440, O. \u0417\u0430\u0441\u0442\u0430\u0432\u043d\u0438\u0439, \u0404. \u041d\u0438\u043a\u043e\u043b\u0430\u0439\u0447\u0443\u043a \/\/ \u041d\u0430\u0443\u043a\u043e\u0432\u0438\u0439 \u0432\u0456\u0441\u043d\u0438\u043a \u041d\u041b\u0422\u0423 \u0423\u043a\u0440\u0430\u0457\u043d\u0438. \u2013 2016. \u2013 \u2116 26(3). \u2013 \u0421. 361\u2013367.<\/li>\n
  2. \u0412\u043e\u043b\u043e\u0448\u0438\u043d \u041c. \u0412. \u0420\u043e\u0437\u0440\u043e\u0431\u043a\u0430 \u043a\u043e\u043c\u0431\u0456\u043d\u043e\u0432\u0430\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c \/ \u041c. \u0412. \u0412\u043e\u043b\u043e\u0448\u0438\u043d\/\/ Technology audit and production reserves. \u2013 \u2013 \u0422\u043e\u043c 3. \u2116 2(47). \u2013 \u0421. 9\u201314.<\/li>\n
  3. \u0411\u0430\u0441\u044e\u043a \u0422. \u0410\u043d\u0430\u043b\u0456\u0437 \u0442\u0430 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044f \u043e\u0441\u043d\u043e\u0432\u043d\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \u043d\u0430 \u043f\u043b\u043e\u0449\u0438\u043d\u0456 \u043f\u0440\u043e\u0435\u043a\u0446\u0456\u0457 \/ \u0422.\u0411\u0430\u0441\u044e\u043a, \u042f. \u041f\u0443\u0448\u043a\u043e \/\/ \u0412\u0456\u0441\u043d\u0438\u043a \u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0443 “\u041b\u044c\u0432\u0456\u0432\u0441\u044c\u043a\u0430 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u043a\u0430”. \u0421\u0435\u0440\u0456\u044f: \u041a\u043e\u043c\u043f\u2019\u044e\u0442\u0435\u0440\u043d\u0456 \u043d\u0430\u0443\u043a\u0438 \u0442\u0430 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0439\u043d\u0456 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457. \u2013 2015. \u2013 \u2116 826. \u2013 \u0421. 291\u2013298.<\/li>\n
  4. \u041f\u043e\u0434\u0441\u043a\u0440\u0435\u0431\u043a\u043e \u041e. \u0421. \u0422\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0432 \u043f\u0440\u043e\u043c\u0438\u0441\u043b\u043e\u0432\u043e\u0441\u0442\u0456 \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \/ \u041e. \u0421. \u041f\u043e\u0434\u0441\u043a\u0440\u0435\u0431\u043a\u043e, \u0414. \u041c. \u041a\u0432\u0430\u0448\u0443\u043a, \u0410. \u041a. \u0411\u0435\u0440\u0456\u0434\u0437\u0435-\u0421\u0442\u0430\u0445\u043e\u0432\u0441\u044c\u043a\u0438\u0439 \/\/\u00a0\u0415\u043a\u043e\u043d\u043e\u043c\u0456\u043a\u0430 \u0442\u0430 \u0434\u0435\u0440\u0436\u0430\u0432\u0430. \u2013 2019. \u2013 \u2116 6. \u2013 \u0421. 46\u201349.<\/li>\n
  5. \u0413\u043e\u043b\u0443\u0431 \u0421. \u0412. \u041f\u043e\u0431\u0443\u0434\u043e\u0432\u0430 \u043c\u043e\u0434\u0435\u043b\u0435\u0439-\u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0442\u043e\u0440\u0456\u0432 \u0437\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430\u043c\u0438 \u0431\u0430\u0433\u0430\u0442\u043e\u0432\u0438\u043c\u0456\u0440\u043d\u043e\u0457 \u043f\u043e\u043b\u044f\u0440\u0438\u0437\u0430\u0446\u0456\u0439\u043d\u043e\u0457 \u043c\u0456\u043a\u0440\u043e\u0441\u043a\u043e\u043f\u0456\u0457 \u0432 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u0441\u0443\u0434\u043e\u0432\u043e-\u043c\u0435\u0434\u0438\u0447\u043d\u043e\u0433\u043e \u0456\u043d\u0442\u0435\u043b\u0435\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043c\u043e\u043d\u0456\u0442\u043e\u0440\u0438\u043d\u0433\u0443 \u0445\u0432\u043e\u0440\u043e\u0431 \u0441\u0435\u0440\u0446\u044f \/ \u0421. \u0412. \u0413\u043e\u043b\u0443\u0431, \u042e. \u041e. \u0423\u0448\u0435\u043d\u043a\u043e, \u041e. \u042f. \u0412\u0430\u043d\u0447\u0443\u043b\u044f\u043a, \u041c. \u0412. \u0422\u0430\u043b\u0430\u0445 \/\/ \u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u043d\u0456 \u043c\u0430\u0448\u0438\u043d\u0438 \u0456 \u0441\u0438\u0441\u0442\u0435\u043c\u0438. \u2013 2018. \u2013 \u2116 3. \u2013 \u0421. 48\u201359.<\/li>\n
  6. \u0413\u0430\u043b\u044c\u0447\u0435\u043d\u043a\u043e \u0412. \u042f. \u041c\u0435\u0442\u043e\u0434\u0438 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043c\u0435\u0442\u0430\u043c\u043e\u0434\u0435\u043b\u0435\u0439: \u0441\u0442\u0430\u043d \u043f\u0438\u0442\u0430\u043d\u043d\u044f \/ \u0412. \u042f. \u0413\u0430\u043b\u044c\u0447\u0435\u043d\u043a\u043e, \u0420. \u0412. \u0422\u0440\u0435\u043c\u0431\u043e\u0432\u0435\u0446\u044c\u043a\u0430, \u0412. \u0412. \u0422\u0438\u0447\u043a\u043e\u0432, \u0410. \u0412. \u0421\u0442\u043e\u0440\u0447\u0430\u043a \/\/ \u0412\u0456\u0441\u043d\u0438\u043a \u0412\u0456\u043d\u043d\u0438\u0446\u044c\u043a\u043e\u0433\u043e \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u0447\u043d\u043e\u0433\u043e \u0456\u043d\u0441\u0442\u0438\u0442\u0443\u0442\u0443. \u2013 2020. \u2013 \u2116 4. \u2013 \u0421. 74\u201388.<\/li>\n
  7. Anastasakis L. The development of self-organization techniques in modelling: A review of the Group Method of Data Handling (GMDH) \/ L. Anastasakis, N. Mort \/\/ The University of Sheffield, United Kingdom. \u2013 2001. \u2013 Research Report no. 813. URL: https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf<\/li>\n
  8. GMDH Shell for Data Science uses the power of the GMDH (Group Method of Data Handling). URL: http:\/\/www.gmdh.net\/ (17.03.2022)<\/li>\n
  9. Ghanadzadeh H. Mathematical model of liquid\u2013liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm \/ H. Ghanadzadeh, M. Ganji, S. Fallahi \/\/ Applied Mathematical Modelling. \u2013 2012. \u2013 Vol. 36. \u2116 9. \u2013 P. 4096\u20134105.<\/li>\n
  10. \u041f\u0456\u0434\u043d\u0435\u0431\u0435\u0441\u043d\u0430 \u0413.\u0410. \u041e\u043d\u0442\u043e\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u0439 \u043f\u0456\u0434\u0445\u0456\u0434 \u0434\u043e \u043f\u043e\u0431\u0443\u0434\u043e\u0432\u0438 \u043c\u0435\u0442\u0430\u043c\u043e\u0434\u0435\u043b\u0456 \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043d\u043e\u0457 \u0433\u0430\u043b\u0443\u0437\u0456 \u0456\u043d\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u043c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f \/ \u0413.\u0410. \u041f\u0456\u0434\u043d\u0435\u0431\u0435\u0441\u043d\u0430 \/\/ \u0423\u0421\u0438\u041c. \u2013 2018. \u2013 \u2116 3. \u2013 \u0421. 18\u201332.<\/li>\n
  11. Betari O. Applying a Model Driven Architecture Approach: Transforming CIM to PIM Using UML \/ O. Betari, S. Filali, A. Azzaoui, M. A. Boubnad \/\/ International Journal of Online and Biomedical Engineering (iJOE). \u2013 2018.\u2013 14(09). \u2013 P. 170\u2013181.<\/li>\n<\/ol>\n

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

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    2. Voloshyn \u041c. V. Development of a combined model of image recognition \/ \u041c. V. Voloshyn \/\/Technology audit and production reserves. – 2019. – Vol. 3. \u2116 2(47). – S. 9-14.<\/li>\n
    3. Basiuk \u0422. Analysis and classification of basic methods of pattern recognition in the projection plane \/ \u0422. Basiuk, Ya. Pushko \/\/ The Bulletin of Lviv Polytechnic National University titled \u201cComputer sciences and information technologies\u201d. – 2015. – \u2116 826. – S. 291-298.<\/li>\n
    4. Podskrebko \u041e. S. Machine learning technologies in industry using image recognition methods \/ \u041e. S. Podskrebko, D. \u041c. Kvashuk, \u0410. \u041a. Beridze-Stakhovskyi \/\/\u00a0Ekonomika ta derzhava. – 2019. – \u2116 6. – S. 46-49.<\/li>\n
    5. Holub S. V. Construction of models-classifiers based on the results of multidimensional polarization microscopy in the technology of forensic intelligent monitoring of heart disease \/ S. V. Holub, Yu. \u041e. Yshenko, \u041e. Ya. Vanchuliak, \u041c. V. Talakh \/\/ Mathematical Machines and Systems. \u2013 2018. – \u2116 3. – S. 48-59.<\/li>\n
    6. Halchenko V. Ya. Methods of creating metamodels: the state of the question \/ V. Ya. Halchenko, R. V. Trembovetska, V. V. Tychkov, \u0410. V. Storchak \/\/ Visnyk of Vinnytsia Polytechnical Institute. – 2020. – \u2116 4. – S. 74-88.<\/li>\n
    7. Anastasakis L. The development of self-organization techniques in modelling: A review of the Group Method of Data Handling (GMDH) \/ L. Anastasakis, N. Mort \/\/ The University of Sheffield, United Kingdom. – 2001. – Research Report no. 813. URL: https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf<\/li>\n
    8. GMDH Shell for Data Science uses the power of the GMDH (Group Method of Data Handling). URL: http:\/\/www.gmdh.net\/ (Accessed on: 17.03.2022)<\/li>\n
    9. Ghanadzadeh H. Mathematical model of liquid\u2013liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm \/ H. Ghanadzadeh, M. Ganji, S. Fallahi \/\/ Applied Mathematical Modelling. – 2012. – Vol. 36. \u2116 9. – P. 4096-4105.<\/li>\n
    10. Pidnebesna H. \u0410. Ontological approach to the construction of metamodels of the subject area of inductive modeling \/ H. \u0410. Pidnebesna \/\/ Control Systems and Computers. – 2018. – \u2116 3. – S. 18-32.<\/li>\n
    11. Betari O. Applying a Model Driven Architecture Approach: Transforming CIM to PIM Using UML \/ O. Betari, S. Filali, A. Azzaoui, M. A. Boubnad \/\/\u00a0International Journal of Online and Biomedical Engineering (iJOE). – 2018.- 14(09). – P. 170-181.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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