On Possibilistic Mean Value, Variance, Covariance and Correlation of Fuzzy Numbers

Carlsson, Christer; Fuller, Robert C.

doi:10.1007/978-3-642-03737-5_2

Cited by 2 publications

(1 citation statement)

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“…In 2005 Carlsson, Fuller and Majlender [3] introduced a measure of possibilistic correlation between fuzzy numbers A and B by their joint possibility distribution C as an average measure of their interaction (introduced earlier by Fuller and Majlender in [12]) compared to their respective marginal variances. C. Carlsson [8] introduced possibilistic mean value, variance, covariance and correlation of fuzzy numbers. S. Rezvani in [16,23] introduced correct ordering of generalized and normal trapezoidal fuzzy numbers in divergence and Median Value.…”

Section: Introductionmentioning

confidence: 99%

Cardinal, Median Value, Variance and Covariance of Exponential Fuzzy Numbers with Shape Function and its Applications in Ranking Fuzzy Numbers

Rezvani¹

2016

IJCIS

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In this paper, the researcher proposed a method to cardinal, median value, variance and covariance of exponential fuzzy numbers with shape function . The covariance used in this method is obtained from the exponential trapezoidal fuzzy number, first by finding mathematical expectation and then calculating the variance of each exponential fuzzy numbers by E(A), E 2 (A) and then finding the possibilistic covariance between fuzzy numbers A and B. We are going to utilize Dubois and another researcher dominance possibility and necessity indices, within a exponential fuzzy numbers with shape function and its applications, in the case of measure of possibilistic correlation between fuzzy numbers A and B by their ranking possibility of their interaction compared to their possibilistic fuzzy number. Finally, we proposed a new method for ranking exponential fuzzy numbers with possibilistic variance between fuzzy numbers A and B. This approach helps avoiding any approximation that may exist due to incorrect comparing between fuzzy numbers and can effectively rank various fuzzy numbers, their images and overcome the shortcomings of the previous techniques and also the proposed approach is very simple and easy to apply in the real life problems. For the validation, the results of the proposed approach are compared with different existing approaches.

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