Assessment of the fuzzy state of complex systems

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dc.contributor.authorYakovenko, A.
dc.date.accessioned2020-08-25T11:47:40Z
dc.date.available2020-08-25T11:47:40Z
dc.date.issued2020
dc.descriptionYakovenko A. Assessment of the fuzzy state of complex systems / A. Yakovenko ; supervisor O. Goloskokov // Black Sea Science 2020 : рroc. of the Intern. Competition of Student Scientific Works. Information Technology, Automation and Robotics / Odessa Nat. Acad. of Food Technologies ; eds. B. Yegorov, M. Mardar, S. Kotlyk [et al.]. – Odessa : ONAFT, 2020. – P. 108–120 : tabl., fig. – Ref.: 12 tit.ru_RU
dc.description.abstractThe paper considers the problem of estimating the state of the enterprise (on example of the IT company). The problem is presented in the form of two problems. The first problem is the aggregation of the initial information and the second problem is the identification of the state of a complex system. To solve the problem of aggregation of initial data authors used the fuzzy cluster analysis, namely the fuzzy k-means method. The results allow to formalize linguistic variables, which are characterized by the term-sets and definition range. The numerical results were approximated by analytical membership functions. The solution of the first task allows to generate a set of possible fuzzy reference situations. Each situation is characterized by the reference informational granule, which contains information about formalized linguistic variables. The second problem was solved by using the method of fuzzy logic in the MATLAB environment. In this test case, the search of the situation in which the IT-company is located was performed. At this stage, the current situation belongs to comparison with each reference situation. In this way, authors determined the most similar reference situation to the current situation. An analysis of the resulting situation allows to argue the state of the IT company. The solution of the second task allowed to establish assessment of IT company state. The theoretical and practical results can improve the efficiency of complex system management.ru_RU
dc.identifier.urihttps://card-file.ontu.edu.ua/handle/123456789/14475
dc.language.isoenru_RU
dc.subjectcomplex system managementru_RU
dc.subjectcondition assessmentru_RU
dc.subjectfuzzy cluster analysisru_RU
dc.subjectfuzzy situational approachru_RU
dc.subjectreference situationsru_RU
dc.subjectinformational granuleru_RU
dc.titleAssessment of the fuzzy state of complex systemsru_RU
dc.typeArticleru_RU
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