A Novel Interval Type-2 Fuzzy System Identification Method Based on the Modified Fuzzy C-Regression Model

In this article, a novel interval type-2 Takagi-Sugeno fuzzy <inline-formula> <tex-math notation="LaTeX">c </tex-math></inline-formula>-regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to descr...

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Bibliographic Details
Published in:	IEEE transactions on cybernetics Vol. 52; no. 9; pp. 9834 - 9845
Main Authors:	Tsai, Shun-Hung, Chen, Yu-Wen
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Clustering algorithms Data models Data points Fuzzy <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c -regression model (FCRM) Fuzzy sets fuzzy system identification Fuzzy systems Identification methods interval type-2 fuzzy systems Least squares method Linear programming Mathematical model Numerical models Parameters Regression models Robustness (mathematics) System identification Takagi–Sugeno (T–S) fuzzy model Uncertainty
ISSN:	2168-2267, 2168-2275, 2168-2275
Online Access:	Get full text
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