Aggregation Using the Fuzzy Weighted Average as Computed by the Karnik-Mendel Algorithms
نویسندگان
چکیده
By connecting work from two different problems— the fuzzy weighted average (FWA) and the generalized centroid of an interval type-2 fuzzy set—a new -cut algorithm for solving the FWA problem has been obtained, one that is monotonically and superexponentially convergent. This new algorithm uses the Karnik–Mendel (KM) algorithms to compute the FWA -cut endpoints. It appears that the KM -cut algorithms approach for computing the FWA requires the fewest iterations to date, and may therefore be the fastest available FWA algorithm to date.
منابع مشابه
On Computing the Fuzzy Weighted Average Using the KM Algorithms
By connecting the fuzzy weighted average (FWA) and the generalized centroid (GC) of an interval type-2 fuzzy set we have arrived at a new αcut algorithm for solving the FWA problem, one that is monotonically and super-exponentially convergent. Our new algorithm uses the Karnik-Mendel (KM) algorithms to compute the FWA α-cut end-points. No faster algorithm for solving this problem exists to-date.
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ورودعنوان ژورنال:
- IEEE Trans. Fuzzy Systems
دوره 16 شماره
صفحات -
تاریخ انتشار 2008