Towards Improved Trapezoidal Approximation to Intersection (Fusion) of Trapezoidal Fuzzy Numbers: Specific Procedure and General Non-Associativity Theorem

摘要

In some cases, our uncertainty about a quantity can be described by an interval of its possible values. If we have two or more pieces of interval information about the same quantity, then we can conclude that the actual value belongs to the intersection of these intervals.In general, we may need a fuzzy number to represent our partial knowledge. A fuzzy number can be viewed as a collection of intervals (alpha-cuts) corresponding to different degrees alpha from [0,1]. In practice, we can only store finitely many alpha-cuts. Usually, we only store the lower and upper alpha-cuts (corresponding to alpha = 0 and alpha = 1) and use linear interpolation -- i.e., use trapezoidal fuzzy numbers. However, the intersection of two trapezoidal fuzzy numbers is, in general, not trapezoidal. One possible approach is to simply take an intersection of lower and upper alpha-cuts, but this approach underestimates the resulting membership function.In this paper, we propose a more accurate approach that uses the Least Squares Method to provide a better linear approximation to the resulting membership function.While this method provides a more accurate trapezoidal description of the intersection, it has its own drawbacks: e.g., this approximation method makes the corresponding u22knowledge fusionu22 operation non-associative. We prove, however, that this u22drawbacku22 is inevitable: specifically, we prove that a perfect solution is not possible, and that any improved trapezoidal approximation to intersection (fusion) of trapezoidal fuzzy numbers leads to non-associativity.