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An application of the fuzzy set theory and fuzzy logic to the problem of predicting the value of goods rests

Autorzy
Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Applying the fuzzy set theory and fuzzy logic, we construct a mathematical model for predicting the value of unrealized goods rests and demonstrate this model on an empirical example showing good correlation with the real data.
Twórcy
autor
  • Department of Higher Mathematics, Lviv Polytechnic National University. 79013 Lviv, Stepana Bandery str., 12
autor
  • Department of Higher Mathematics, Lviv Polytechnic National University. 79013 Lviv, Stepana Bandery str., 12
Bibliografia
  • 1. Liu Baoding, and Yian-Kui Liu. 2002. Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on fuzzy systems Vol. 10, Nos. 4, 445-450.
  • 2. J. van den Berg, U. Kaymak, and W.M. van den Bergh. 2004. Financial markets analysis by using a probabilistic fuzzy modelling approach. International Journal of Approximate Reasoning.Vol. 35-3, 291–305
  • 3. Bojadziev G. and Bojadziev M. 1995. Fuzzy sets and fuzzy logic, applications. Vorld scientific. Mathematics.
  • 4. Juravljova I. 2008. Application of fuzzy sets theory to the problems of an intellectual consumer’s capital management. Scientific papers Donetsk NTU. Vol. 33-2, 126-131. (in Ukrainian)
  • 5. Gil-Lafuente A.M. 2005. Fuzzy Logic in Financial Analysis. Studies in Fuzziness and Soft Computing, Vol. 175
  • 6. Korshevnjuk L. and Bydjuk P. 2003. Solution of the problem of distribution of investments on the basis of fuzzy logic conclusion. System researches and information technologies. Vol. 2, 34-42 (in Ukrainian)
  • 7. Marushchak S. 2011. Methodology of estimation of enterprise’s economic security based on the fuzzy sets theory. Announcer Hmelnytsky NU. Vol 1, Nos. 5, 16-21. (in Ukrainian)
  • 8. Matvijchuk A. 2010. Financial firmness modeling of enterprises with application of fuzzy logic theory, neural networks and discriminant analysis. Announcer NAS , Vol. 9. (in Ukrainian)
  • 9. Novak V., Perfiljeva I., and Mochkorj I. 2006. Mathematical principles of the fuzzy logic. Fizmatlit. (in Russia)
  • 10. Rotshtein O.P. and Jupanova M.O., and Sheverda V.M. 1994. Differential diagnostics of ischemic heart trouble on the basis of fuzzy logic. Announcer VPI Vol.3. Nos.32. (in Ukrainian)
  • 11. Siavavko M. and Rybytska O. 2000. A mathematical modeling in the terms of uncertainty . Ukranian technologies. (in Ukrainian)
  • 12. Siavavko M. 2007. Information system “Fuzzy expert”. Lviv National University. (in Ukrainian)
  • 13. Siavavko M. 2011. Mathematic of hidden features. National university “Ostrog Akademy”. (in Ukrainian)
  • 14. Shang K., and Hossen Z. 2013. Applying Fuzzy Logic to Risk Assessment and Decision-Making. Canadian institute of actuaries
  • 15. Telenik S. and Bidjuk P., and Korshevnjuk L., and Hmeljuk V. 2008. The fuzzy logic based approach for problem of multicriterion expert estimation Problems of programming. Vol. 4, 73-83. (in Ukrainian)
  • 16. Zadeh L.A. 1976. The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Information Sciences.Vol.9:3. 43-80.
  • 17. Zadeh L.A. 1983. The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems. Fuzzy Sets and Systems. Vol.11. 199-227.
  • 18. Zadeh L.A. 1996. Fuzzy Logic = Computing With Words. IEEE Transactions on Fuzzy Systems. Vol. 4. 103-111.
  • 19. Zapounidis C., Pardalos P.M., and Baourakis G.. 2001. Fuzzy Sets in Management, Economics and Marketing, World Scientific.
  • 20. Zimmermann H. 1991. Fuzzy Set Theory and it’s Applications. Kluner Academic Publishers.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e950d45f-f14d-40d3-82e7-bab9a2a185cd
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