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Information technology for treatment of results expert estimation with fuzzy character input data

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Języki publikacji
EN
Abstrakty
EN
The questions of processing expert information provided by the group of experts are considered. In the treatment group of expert opinion there is a problem obtaining a generalized result. It is assumed that the information is a set of qualitative and quantitative features (alternatives), described by linguistic concepts. Statistical methods of expert’s data processing are quite complicated when the expert’s answers have form of ranking or separation, and quite simple, if the answers are the results of independent pairwise comparisons. In this article are proposed to carry out processing of expert information by methods of the fuzzy sets theory. Using this theory are developed a method for determining the qualifications of the expert based on his length of service and number of expertise conducted by him, the results of which are accurate. The developed method is based on the construction of a system on fuzzy logic with fuzzifikator and defuzzifikator. For processing expert’s estimates are suggested each alternative presented in the linguistic variable form and evaluate it by assigning a group of experts of membership functions each term by the direct method. Obtaining a generalized assessment based on all expert’s estimates going on with regard to their competence. In this paper a method of ranking fuzzy alternatives are propose. Based on the developed methods of data processing was designed an automated system that allows to determine the experts qualification, generalized result of expert group evaluation and of ranking alternatives. The developed technology is applicable to any subject area, where it is necessary to analyze alternatives for many of the criteria based on processing of expert estimations.
Twórcy
  • University of Customs and Finance, Dnipro, Ukraine
Bibliografia
  • 1. Angeli C., 2010. Diagnostic Expert Systems: From Expert’s Knowledge to Real-Time Systems, TMRF e-Book Advanced Knowledge Based Systems: Model, Applications & Research. Vol. 1, 50–73.
  • 2. Lytvyn V., 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. An international quarterly journal Vol. 2, №1. 31-37.
  • 3. Korneev V., Gareyev A., Vasjutin S., Reich V., 2000. Databases. Intelligent processing of information. М.: Knowledge, 352. (in Russian).
  • 4. Kaklauskasa A., Zavadskasbc E., Gargasaited L., 2004. Expert and knowledge systems and databases of the best practice. Vol. 10, 88-95.
  • 5. Filatov V., 2014. Fuzzy models presentation and realization by means of relational systems. ECONTECHMOD. An international quarterly journal Vol. 3, №3. 99-102.
  • 6. Ivanek J., 1991. Representation of expert knowledge as a fuzzy axiomatic theory. International Journal of General Systems. Vol. 20, 55–58.
  • 7. Gong J., Liu L., 2010. Representing and measuring experts knowledge based on knowledge network. Studies in Science of Science. 28 (10), 1521–1530.
  • 8. Daintith J., 2009. IT. A Dictionary of Physics, Oxford University Press, retrieved 1 August 2012.
  • 9. Orlov A., 2004. Applied Statistics. М.: Exam, 656. (in Russian).
  • 10. Ryabushkin T., Baklanov G., Volkov A., 1997. Statistical methods of analysis of expert estimations. Proc. Sciences. Art. Scientific notes on statistics. Vol. 29. М.: Science, 385. (in Russian).
  • 11. Litvak B., 1982. Expert information: Methods for the preparation and analysis. М.: Radio and Communications, 184. (in Russian).
  • 12. Hughes C., 1989. The representation of uncertainty in medical expert systems. Medical Informatics. Vol. 14, 269-279.
  • 13. Melihov A., Bernstein L. and Korovin S., 1990. Situational advising system with fuzzy logic. М.: Science. 272. (in Russian).
  • 14. Kovalyshyn O., Gabriel Yu., 2014. Development of a management systems model of automatic control by using fuzzy logic. ECONTECHMOD. An international quarterly journal. Vol. 3, №. 4. 87-90.
  • 15. Pavlov A., Sokolov B., 2005. Methods of processing expert information: study guide. St. Petersburg: State University of Aerospace Instrumentation. 42. (in Russian).
  • 16. Snityuk V., Rifat Mohammed Ali, 2000. Model methods for determining the competence of experts on the basis of the axioms of unbiasedness. Bulletin of Cherkasy Engineering and Technological Institute. №4, 121-126. (in Russian).
  • 17. Ulianovskaya Yu., 2013. Ranking method of fuzzy alternatives in processes of transfer and information processing in computer systems. Herald of Khmelnytskyi national university. №1(197), 130-134. (in Russian).
  • 18. Shtovba S., 2007. Design of fuzzy systems by means of MATLAB. М.: Hotline – Telecom, 288. (in Russian).
  • 19. Zadeh L., 1976. The concept of linguistic variable and its application to take close resheniy. -M.: Mir, 165. (in Russian).
  • 20. Ulianovskaya Yu., 2014. Method for determination of qualifications of the experts participating in the expert survey. Herald of Kherson national technical university. № 1(48), 96-101. (in Russian).
  • 21. Rybytska O., Vovk M., 2014. An Application of the Fuzzy Set Theory and Fuzzy Logic to the Problem of Predicting the Value of Goods Rests. ECONTECHMOD. An International Quarterly Journal. Vol. 3, №2, 65-69.
  • 22. Introducing JSON. Available online at: <http://json.org/>
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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