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BUSINESS INFORM №10-2015

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27

Section: Economic and Mathematical Modeling
UDC 65.016.8
Klebanova T. S., Gvozdytskyi V. S.
Assessment of Propensity of Enterprises towards Bankruptcy by on the Basis of Methods of Fuzzy Logic and Fuzzy Neural Networks (p. 165 - 170)

The article is aimed at development of models for assessment of enterprises' propensity towards bankruptcy by the methods of fuzzy logic and fuzzy neural networks. Use of a system of indicators, included in the different financial groups, providing a complex description of the financial condition of enterprise, has been substantiated. The publication describes the major advantages of the apparatus of fuzzy logic in assessing the threat of bankruptcy. In order to build a fuzzy model, two linguistic variables were formed and classified: class of financial crisis and the corresponding status of enterprise. Implementation of the model has been tested with regard to activities of five enterprises in the engineering industry sector of Ukraine. The results of modeling have been analyzed and compared by methods of fuzzy logic and neuro-fuzzy networks. Recommendations on forecasting the future financial status of enterprises have been offered.
Keywords: analysis, bankruptcy, crisis, model, fuzzy logic, fuzzy neural networks, assessment, financial status
Tabl.: 5. Formulae: 1. Bibl.: 8.

Klebanova Tamara S. – Doctor of Sciences (Economics), Professor, Professor, Department of Economic Cybernetics, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Gvozdytskyi Vitalii S. – Candidate of Sciences (Economics), Lecturer, Department of Economic Cybernetics, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]

Article is written in Russian
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Reference to this article:
Klebanova, Tamara S., and Gvozdytskyi, Vitalii S. (2015) “Assessment of Propensity of Enterprises towards Bankruptcy by on the Basis of Methods of Fuzzy Logic and Fuzzy Neural Networks.” Business Inform 10:165–170.


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