Interval type-2 modified fuzzy c-regression model clustering algorithm in TS Fuzzy Model identification

This paper introduces an interval type-2 modified fuzzy c-regression model (IT2MFCRM) clustering algorithm for identifying the structure in TS Fuzzy Model (TSFM). A scaling factor has been used for identifying the model parameters of interval type-2 fuzzy set. Once, the type-1 MFCRM clustering algor...

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Bibliographic Details
Published in2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1671 - 1676
Main Authors Dam, Tanmoy, Deb, Alok Kanti
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Clustering algorithms
Data models
Fuzzy sets
Linear programming
Mathematical model
Partitioning algorithms
Uncertainty
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DOI10.1109/FUZZ-IEEE.2016.7737891

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Summary:This paper introduces an interval type-2 modified fuzzy c-regression model (IT2MFCRM) clustering algorithm for identifying the structure in TS Fuzzy Model (TSFM). A scaling factor has been used for identifying the model parameters of interval type-2 fuzzy set. Once, the type-1 MFCRM clustering algorithm has been performed to obtain the premise parameters of membership function, then scaling factor has been used to obtain type-2 premise parameters. Once the type-2 membership function is obtained, then type reduction technique has been used for identifying the coefficients of consequence parameters. Orthogonal Least Square (OLS) method has been applied for determining the consequence parameters. Finally, the IT2MFCRM based TS fuzzy model has been validated on two benchmark examples.
DOI:10.1109/FUZZ-IEEE.2016.7737891