A Modified Method to Measure Similarity of Generalized Fuzzy Numbers

Chun Jiang Zhao

doi:10.4028/www.scientific.net/AMR.159.393

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 159A Modified Method to Measure Similarity of...

A Modified Method to Measure Similarity of Generalized Fuzzy Numbers

Abstract:

A modified method to measure the similarity between the generalized fuzzy numbers based on Wasserstein distance is proposed. The method is more fully considered the difference of the generalized fuzzy numbers, especially the shape difference, by Wasserstein distance. The results of comparing the eighteen sets of generalized fuzzy numbers show that the method can overcome the drawbacks of the existing methods, and calculate the similarity measure excellently.

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Periodical:

Advanced Materials Research (Volume 159)

Pages:

393-398

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.159.393

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Online since:

December 2010

Authors:

Chun Jiang Zhao

Keywords:

Fuzzy Number, Similarity Measure, Wasserstein Distance

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References

[1] Y. Deng and W. K. Shi, A modified aggregation of fuzzy opinions under group decision making, Cybern. Syst.: Int. J, vol. 34, no. 3, pp.207-216, (2003).

Google Scholar

[2] H. S. Lee, Optimal consensus of fuzzy opinions under group decision making environment, Fuzzy sets Syst., vol. 132, no. 3, pp.303-315, (2002).

DOI: 10.1016/s0165-0114(02)00056-8

Google Scholar

[3] S. M. Chen, Aggregating fuzzy opinions in the group decision-making environment, Cybern. Syst.: Int. J, vol. 27, no. 4, pp.449-472, (1998).

Google Scholar

[4] C. H. Hsieh and S. H. Chen, Similarity of generalized fuzzy numbers with graded mean integration representation, in Proc. 8th Int. Fuzzy Systems Association World Congr., vol. 2, Taiwan, Republic of China, 1999, pp.551-555.

Google Scholar

[5] S. J. Chen and S. M. Chen, Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers, IEEE Trans. Fuzzy Syst., vol. 11, no. 1, pp.45-56, (2003).

DOI: 10.1109/tfuzz.2002.806316

Google Scholar

[6] Otto F, Villani C., Generalization of an inequality by Talagrand and links with the logarithmic Sobolev inequality, Journal of functional analysis. 2000, 6(2) pp: 361-400.

DOI: 10.1006/jfan.1999.3557

Google Scholar

[7] Hoffman J R, Mahler R P S., Multitarget miss distance and its applications, Information Fusion, 2002, Proceedings of the Fifth International Conference. 2002, 7 (1) pp: 149 – 155.

DOI: 10.1109/icif.2002.1021144

Google Scholar