DocumentCode
1206549
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
Volume
17
Issue
4
fYear
2009
Firstpage
923
Lastpage
934
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;
fLanguage
English
Journal_Title
Fuzzy Systems, IEEE Transactions on
Publisher
ieee
ISSN
1063-6706
Type
jour
DOI
10.1109/TFUZZ.2008.924329
Filename
4505357
Link To Document