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Decision Support Systems
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A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers
Applied Intelligence
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
An algorithm for solving fuzzy maximal flow problems using generalized triangular fuzzy numbers
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
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Design of real-time fuzzy bus holding system for the mass rapid transit transfer system
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
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Advances in Fuzzy Systems - Special issue on Real-Life Applications of Fuzzy Logic
Fuzzy risk analysis based on a geometric ranking method for generalized trapezoidal fuzzy numbers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we present a new approach for fuzzy risk analysis based on the ranking of fuzzy numbers. First, we propose a new method for ranking fuzzy numbers using the @a-cuts, the belief feature and the signal/noise ratios, where @a@?[0,1]. The proposed method for ranking fuzzy numbers calculates the signal/noise ratio of each @a-cut of a fuzzy number to evaluate the quantity and the quality of a fuzzy number, where the signal and the noise are defined as the middle-point and the spread of each @a-cut of a fuzzy number, respectively. We use the value of @a as the weight of the signal/noise ratio of each @a-cut of a fuzzy number to calculate the ranking index of each fuzzy number. The proposed method can rank any kinds of fuzzy numbers with different kinds of membership functions. Then, we apply the proposed fuzzy ranking method to propose a fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. Because the proposed fuzzy risk analysis method considers the degrees of confidence of decision makers' opinions, it is more flexible than the existing methods.