Title :
Enhanced Karnik--Mendel Algorithms
Author :
Wu, Dongrui ; Mendel, Jerry M.
Author_Institution :
Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
The Karnik-Mendel (KM) algorithms are iterative procedures widely used in fuzzy logic theory. They are known to converge monotonically and superexponentially fast; however, several (usually two to six) iterations are still needed before convergence occurs. Methods to reduce their computational cost are proposed in this paper. Extensive simulations show that, on average, the enhanced KM algorithms can save about two iterations, which corresponds to more than a 39% reduction in computation time. An additional (at least) 23% computational cost can be saved if no sorting of the inputs is needed.
Keywords :
computational complexity; fuzzy logic; iterative methods; computational cost; enhanced Karnik-Mendel algorithms; fuzzy logic theory; iterative procedures; Enhanced Karnik--Mendel (EKM) algorithms; Enhanced Karnik-Mendel algorithms; Karnik--Mendel (KM) algorithms; Karnik-Mendel algorithms; interval type-2 fuzzy sets; interval type-2 fuzzy sets (IT2FSs); novel weighted averages; type-reduction; uncertainty measures;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2008.924329