An accurate comparison of type-reduction algorithms for interval type-2 fuzzy sets using simulated data

DOI

10.2991/ifmca-16.2017.63How to use a DOI?

Keywords

Interval type-2 fuzzy sets(IT2FSs), Type-reduction; Karnik–Mendel (KM) algorithms, Enhanced Karnik–Mendel (EKM) algorithms, Iterative Algorithm with Stop Condition (IASC), Enhanced Iterative Algorithm with Stop Condition(EIASC)

Abstract

In order to find the best type-reduction algorithm of interval type-2 fuzzy sets with different characteristics, this paper makes a comparative analysis of KM, EKM, IASC and EIASC. Experiments are carried out on three type-2 fuzzy sets to compare the four algorithms.The results show that the four algorithms can accurately find the switching points, and the EIASC algorithm is the most efficient. This study provides an accurate and reliable comparative analysis for evaluating the applicability of the algorithm on different data characteristics.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haihua Xing
AU  - Hongyan Lin
AU  - Chunhui Song
PY  - 2017/03
DA  - 2017/03
TI  - An accurate comparison of type-reduction algorithms for interval type-2 fuzzy sets using simulated data
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 408
EP  - 415
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.63
DO  - 10.2991/ifmca-16.2017.63
ID  - Xing2017/03
ER  -