A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words

This article compares three methods [Interval Approach (IA), Enhanced Interval Approach (EIA) and Hao–Mendel Approach (HMA)] for estimating (synthesizing) an interval type-2 fuzzy set (IT2 FS) model for a word, beginning with data that are collected from a group of subjects, or from a single subject...

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Bibliographic Details
Published inGranular computing (Internet) Vol. 1; no. 1; pp. 59 - 69
Main Author Mendel, Jerry M.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.03.2016
Subjects
Artificial Intelligence
Computational Intelligence
Engineering
Original Paper
Interval approach
Interval type-2 fuzzy sets
Enhanced interval approach
Hao–Mendel approach
Computing with words
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Summary:This article compares three methods [Interval Approach (IA), Enhanced Interval Approach (EIA) and Hao–Mendel Approach (HMA)] for estimating (synthesizing) an interval type-2 fuzzy set (IT2 FS) model for a word, beginning with data that are collected from a group of subjects, or from a single subject. It summarizes the stages for each of the methods in tables so it is possible to compare the steps of each stage side-by-side. It also demonstrates, by means of an example of three words, that using more information contained in the collected data intervals is equivalent to reducing the uncertainty in the IT2 FS model. It recommends the HMA because it uses more information contained in the collected data intervals than does the IA or the EIA, and because it is the only method to-date that leads to normal IT2 FSs. Such fuzzy sets are easier to compute with than are non-normal IT2 FSs.
ISSN:2364-4966
2364-4974
DOI:10.1007/s41066-015-0009-7