Modification on enhanced Karnik–Mendel algorithm

Salaken, Syed Moshfeq, Khosravi, Abbas and Nahavandi, Saeid 2016, Modification on enhanced Karnik–Mendel algorithm, Expert systems with applications, vol. 65, pp. 283-291, doi: 10.1016/j.eswa.2016.08.055.

Attached Files
Name Description MIMEType Size Downloads

Title Modification on enhanced Karnik–Mendel algorithm
Author(s) Salaken, Syed MoshfeqORCID iD for Salaken, Syed Moshfeq
Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name Expert systems with applications
Volume number 65
Start page 283
End page 291
Total pages 9
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-12-15
ISSN 0957-4174
Keyword(s) Interval type 2 fuzzy logic
Type reduction
Karnik–Mendel algorithm
Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
Computer Science
Karnik-Mendel algorithm
Summary Karnik–Mendel(KM) algorithm and its enhancements are among the most popular type reduction algorithms in the literature. Enhanced KM (EKM) algorithm is computationally fast and can quickly locate left and right switch points in just a few iteration. This paper proposes a subtle yet very effective modification to EKM algorithm to further improve its computational requirement. The modification relates to how the initial right switch point is determined. Comprehensive simulation results for different cases and scenarios provide the statistical proof for the validity of conclusions drawn on the superiority of the proposed initialization compared to initialization used in the original EKM algorithm. The superiority is quantitatively measured in the number of saved iterations and convergence speed.
Language eng
DOI 10.1016/j.eswa.2016.08.055
Field of Research 099999 Engineering not elsewhere classified
01 Mathematical Sciences
08 Information And Computing Sciences
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Elsevier Ltd
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 6 times in TR Web of Science
Scopus Citation Count Cited 6 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 484 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 31 Mar 2017, 13:19:04 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact