Knowledge representation using linguistic fuzzy relations
Knowledge representation using linguistic fuzzy relations
A method for inference in approximate reasoning based on interval-valued fuzzy sets
Fuzzy Sets and Systems
Efficiently generating test vectors with state pruning
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
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
An extension of the Promethee II method based on generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Analogy-based software effort estimation using Fuzzy numbers
Journal of Systems and Software
Analyzing fuzzy risk based on similarity measures between interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fuzzy rule-based approach for screening international distribution centres
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Rational decision making models with incomplete weight information for production line assessment
Information Sciences: an International Journal
Fuzzy Optimization and Decision Making
Information Sciences: an International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
In this paper, we present a new method for fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers. First, we present a new similarity measure between interval-valued fuzzy numbers. It combines the concepts of geometric distance, the perimeter, the height and the center-of-gravity-points of interval-valued fuzzy numbers for calculating the degree of similarity between interval-valued fuzzy numbers. We also prove some properties of the proposed similarity measure. We make an experiment to use nine sets of interval-valued 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. We also propose a new division operator and an interval-valued fuzzy number adjustment algorithm. Based on the proposed similarity measure, new division operator and adjustment algorithm, 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 interval-valued fuzzy numbers. The proposed method provides a useful way to deal with fuzzy risk analysis problems.