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Interval Distance for Similarity Measure Between Interval-valued Fuzzy Numbers

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초록

Fuzzy systems such as fuzzy linear systems, fuzzy differential equations, fuzzy linear programming, fuzzy decision making, and other topics are used to study various problems. In general, fuzzy numbers are used to represent the various data in fuzzy systems. Many researches have worked on that measure the distance or the different between two fuzzy numbers as follows: computing distance between two generalized fuzzy numbers, distance measuring between interval numbers, measuring between fuzzy numbers in the Euclidean space, measuring include vagueness, distance measuring between Gaussian type fuzzy numbers, measuring interval metric distance include ⍺-cut, and so on. Distance measure or different are very essential tool in a variety of fields. In this paper, we propose a method to measure the interval distance of two interval-valued fuzzy numbers. The method is based on the measure of interval distance on the research of M. A. Firozja which can apply to two interval fuzzy numbers. Also, we show the properties of interval distance and proofs of them. The interval distance between interval-valued fuzzy numbers can provide us with a useful way to measure the similarity for handling various fuzzy systems which represent the data as interval-valued fuzzy number.

Abstract

Fuzzy systems such as fuzzy linear systems, fuzzy differential equations, fuzzy linear programming, fuzzy decision making, and other topics are used to study various problems. In general, fuzzy numbers are used to represent the various data in fuzzy systems. Many researches have worked on that measure the distance or the different between two fuzzy numbers as follows: computing distance between two generalized fuzzy numbers, distance measuring between interval numbers, measuring between fuzzy numbers in the Euclidean space, measuring include vagueness, distance measuring between Gaussian type fuzzy numbers, measuring interval metric distance include ⍺-cut, and so on. Distance measure or different are very essential tool in a variety of fields. In this paper, we propose a method to measure the interval distance of two interval-valued fuzzy numbers. The method is based on the measure of interval distance on the research of M. A. Firozja which can apply to two interval fuzzy numbers. Also, we show the properties of interval distance and proofs of them. The interval distance between interval-valued fuzzy numbers can provide us with a useful way to measure the similarity for handling various fuzzy systems which represent the data as interval-valued fuzzy number.

참고문헌 (11)

  1. L. Tran, "Comparison of fuzzy numbers using a fuzzy distance measure", Fuzzy Sets and Systems, 130(3), pp.331~341 (2002)
  2. P. Diamond, "Fuzzy sets in decision analysis, operations research and statistics", pp.349~387 (1998), Kluwer Academic Publishers
  3. Yang, Ko, "On cluster-wise fuzzy regression analysis, IEEE Transactions on Systems", Man, and Cybernetics, Part B, 27 (1997)
  4. R. Xu, "Multidimensional least-squares fitting with a fuzzy model", Fuzzy Sets and Systems, 119, pp.215~223 (2001)
  5. C. H. Cheng, "A new approach for ranking fuzzy numbers by distance method", Fuzzy Sets and Systems, 95, pp.307~317 (1998)
  6. T. Allahviranloo, "Ranking of fuzzy numbers by a new metric", Soft Computing, 14, pp.773~782 (2010)
  7. C. Chakraborty, "A theoretic development on fuzzy distance measure for fuzzy numbers", Mathematics and Computer Modelling, 43, pp.254~261 (2006)
  8. D. Guha, "A new approach to fuzzy distance measure and similarity measure between two generalized fuzzy numbers", Applied Soft Computing, 10, pp.90~99 (2010)
  9. M. A. Firozja, "The similarity measure of generalized fuzzy numbers based on interval distance", Applied Mathematics Letters, 25, pp.1528~1534 (2012)
  10. D. Dubois, "Fuzzy sets and systems: theory and applications" (1980), Academic Press
  11. S. Chen, "Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers", Computers and Mathematics with Applications, 22(8), pp.1670~1685 (2007)

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