Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers
Applied Intelligence
Expert Systems with Applications: An International Journal
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers
Expert Systems with Applications: An International Journal
An extension of the Promethee II method based on generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Analogy-based software effort estimation using Fuzzy numbers
Journal of Systems and Software
Computers and Industrial Engineering
TOPSIS with fuzzy belief structure for group belief multiple criteria decision making
Expert Systems with Applications: An International Journal
An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Computational intelligence algorithms analysis for smart grid cyber security
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Advances in Artificial Intelligence
Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach
Expert Systems with Applications: An International Journal
Advances in Fuzzy Systems - Special issue on Real-Life Applications of Fuzzy Logic
Hi-index | 12.06 |
In this paper, we present a new method for fuzzy risk analysis based on similarity measures between generalized fuzzy numbers. First, we present a new similarity measure between generalized fuzzy numbers. It combines the concepts of geometric distance, the perimeter and the height of generalized fuzzy numbers for calculating the degree of similarity between generalized fuzzy numbers. We also prove some properties of the proposed similarity measure. We make an experiment to use 15 sets of generalized fuzzy numbers to compare the experimental results of the proposed method with the existing similarity measures. The proposed method can overcome the drawbacks of the existing similarity measures. Based on the proposed similarity measure between generalized fuzzy numbers, we present a new fuzzy risk analysis algorithm for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by generalized fuzzy numbers. The proposed method provides a useful way to deal with fuzzy risk analysis problems.