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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Veröffentlicht in:IEEE transactions on cybernetics Jg. 52; H. 9; S. 9834 - 9845
Hauptverfasser: Tsai, Shun-Hung, Chen, Yu-Wen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy <inline-formula> <tex-math notation="LaTeX">c </tex-math></inline-formula>-regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box-Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3072851