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Issue:Intuitionistic fuzzy random variable

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http://ifigenia.org/wiki/issue:nifs/21/1/69-80
Title of paper: Intuitionistic fuzzy random variable
Author(s):
R. Parvathi
Department of Mathematics, Vellalar College for Women, Erode – 638 012, Tamilnadu, India
paarvathis@rediffmail.com
C. Radhika
Department of Mathematics, Vellalar College for Women, Erode – 638 012, Tamilnadu, India
radhi_math@rediffmail.com
Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 21, 2015, Number 1, pages 69—80
Download:  PDF (223  Kb, File info)
Abstract: Information available in real life is, sometimes not precise, affected by various source of uncertainty, imprecision and vagueness. In a statistical survey, uncertainty contained in the data is itself an obstacle. Fuzzy set is the most effective tool to describe imprecise data through linguistic variables. Prof.Atanassov further generalized fuzzy sets into intuitionistic fuzzy sets to model vagueness present in the natural language. In this paper, intuitionistic fuzzy number is defined as a generalization ofWu’s fuzzy number. Further, intuitionistic fuzzy random variable is defined and some of its properties are discussed. In addition, intuitionistic fuzzy statistical tools are described with suitable illustrations.
Keywords: Intuitionistic fuzzy set, Intuitionistic fuzzy number, Intuitionistic fuzzy random variable, Intuitionistic fuzzy statistical tools, Accuracy.
AMS Classification: 03E72.
References:
  1. Atanassov, K. T. (1999) Intuitionistic Fuzzy Sets: Theory and Applications, Springer Physica–Verlag, Heidelberg.
  2. Atannasov, K. T., & Gargov, G. (1989) Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31(3), 343–349.
  3. Buckley, J. J. (2006) Fuzzy Probabilities and Statistics, Springer, Berlin.
  4. Sun, C.-M., &Wu, B. (2013) New Statistical approaches for fuzzy data, International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 15, 89–106.
  5. Sivaraman, G., Lakshmana Gomathi Nayagam, V., & Ponalagusamy, R. (2012) Intuitionistic fuzzy Interval information system, International Journal of Computer Theory and Engineering, 4(3), 459–461.
  6. Fisz, M. (1963) Probability Theory and Mathematical Statistics,Wiley Publication, London.
  7. Nguyen, H., &Wu, B. (2006) Fundamentals of Statistics with Fuzzy data, Springer, Heidelberg.
  8. Saeidifar, A. (2013) New approximations of fuzzy numbers and their applications, Journal of Applied Science and Agriculture, 7(13), 911–931.
  9. Zadeh, L. A. (1965) Fuzzy sets, Information and Control, 8, 338–353.
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