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Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach | IEEE Journals & Magazine | IEEE Xplore

Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach


Abstract:

This paper presents a very practical type-2-fuzzistics methodology for obtaining interval type-2 fuzzy set (IT2 FS) models for words, one that is called an interval appro...Show More

Abstract:

This paper presents a very practical type-2-fuzzistics methodology for obtaining interval type-2 fuzzy set (IT2 FS) models for words, one that is called an interval approach (IA). The basic idea of the IA is to collect interval endpoint data for a word from a group of subjects, map each subject's data interval into a prespecified type-1 (T1) person membership function, interpret the latter as an embedded T1 FS of an IT2 FS, and obtain a mathematical model for the footprint of uncertainty (FOU) for the word from these T1 FSs. The IA consists of two parts: the data part and the FS part. In the data part, the interval endpoint data are preprocessed, after which data statistics are computed for the surviving data intervals. In the FS part, the data are used to decide whether the word should be modeled as an interior, left-shoulder, or right-shoulder FOU. Then, the parameters of the respective embedded T1 MFs are determined using the data statistics and uncertainty measures for the T1 FS models. The derived T1 MFs are aggregated using union leading to an FOU for a word, and finally, a mathematical model is obtained for the FOU. In order that all researchers can either duplicate our results or use them in their research, the raw data used for our codebook examples, as well as a MATLAB M-file for the IA, have been put on the Internet at: http://sipi.usc.edu/ ~ mendel.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 16, Issue: 6, December 2008)
Page(s): 1503 - 1521
Date of Publication: 26 August 2008

ISSN Information:


I. Introduction

Zadeh [37], [38] proposed the paradigm of computing with words (CWW), i.e., “CWW is a methodology in which the objects of computation are words and propositions drawn from a natural language.” CWW is fundamentally different from the traditional expert systems that are tools to realize an intelligent system but are not able to process natural language because it is imprecise, uncertain, and partially true.

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References

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