Abstract
This paper presents an improved method to compute the degree of similarity between generalized trapezoidal fuzzy numbers. The proposed similarity measure contains many features of fuzzy numbers such as geometric distance, center of gravity (COG), area, perimeter, and height. The previous methods are criticized via presenting some examples. In addition, the performance of the proposed methods is compared by the existing similarity measures using twenty different sets of generalized trapezoidal fuzzy numbers. Furthermore, the proposed method is used for fuzzy risk analysis based on similarity measures. Finally, an example is introduced to illustrate the fuzzy risk analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 478-486 |
| Number of pages | 9 |
| Journal | Applied Soft Computing Journal |
| Volume | 52 |
| DOIs | |
| Publication status | Published - 1 Mar 2017 |
Keywords
- Fuzzy risk analysis
- Generalized trapezoidal fuzzy numbers
- Linguistic terms
- Similarity measure
Cite this
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An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis. / Khorshidi, Hadi Akbarzade; Nikfalazar, Sanaz.
In: Applied Soft Computing Journal, Vol. 52, 01.03.2017, p. 478-486.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis
AU - Khorshidi, Hadi Akbarzade
AU - Nikfalazar, Sanaz
PY - 2017/3/1
Y1 - 2017/3/1
N2 - This paper presents an improved method to compute the degree of similarity between generalized trapezoidal fuzzy numbers. The proposed similarity measure contains many features of fuzzy numbers such as geometric distance, center of gravity (COG), area, perimeter, and height. The previous methods are criticized via presenting some examples. In addition, the performance of the proposed methods is compared by the existing similarity measures using twenty different sets of generalized trapezoidal fuzzy numbers. Furthermore, the proposed method is used for fuzzy risk analysis based on similarity measures. Finally, an example is introduced to illustrate the fuzzy risk analysis.
AB - This paper presents an improved method to compute the degree of similarity between generalized trapezoidal fuzzy numbers. The proposed similarity measure contains many features of fuzzy numbers such as geometric distance, center of gravity (COG), area, perimeter, and height. The previous methods are criticized via presenting some examples. In addition, the performance of the proposed methods is compared by the existing similarity measures using twenty different sets of generalized trapezoidal fuzzy numbers. Furthermore, the proposed method is used for fuzzy risk analysis based on similarity measures. Finally, an example is introduced to illustrate the fuzzy risk analysis.
KW - Fuzzy risk analysis
KW - Generalized trapezoidal fuzzy numbers
KW - Linguistic terms
KW - Similarity measure
UR - http://www.scopus.com/inward/record.url?scp=85006835913&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2016.10.020
DO - 10.1016/j.asoc.2016.10.020
M3 - Article
VL - 52
SP - 478
EP - 486
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
SN - 1568-4946
ER -