Knowledge representation using linguistic fuzzy relations
Knowledge representation using linguistic fuzzy relations
Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers
Applied Intelligence
A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Unified forms of Triple I method
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
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
Information Sciences: an International Journal
Information Sciences: an International Journal
A fuzzy TOPSIS model via chi-square test for information source selection
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Fuzzy risk analysis based on a geometric ranking method for generalized trapezoidal fuzzy numbers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
At present, some researchers provide a type of fuzzy risk analysis algorithms for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by trapezoidal fuzzy numbers. In those algorithms, the main operations are two: one is arithmetic operators of the trapezoidal fuzzy numbers; the other is the similarity of the trapezoidal fuzzy numbers. However, the arithmetic operators of some algorithms do not satisfy some properties of the trapezoidal fuzzy numbers and the similarity of the trapezoidal fuzzy numbers does not coincide with the intuition of the human being. Due to this situation, in this paper, we present an efficient approach for fuzzy risk analysis based on some new arithmetic operators of the trapezoidal fuzzy numbers and propose a new similarity of the trapezoidal fuzzy numbers to deal with fuzzy risk analysis problems. At the same time, we make an experiment to use 30 sets of trapezoidal fuzzy numbers to compare the experimental results of our proposed approach with the existing similarity measures. At last, we use an example to illustrate the efficiency of the new approach.