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- W2362020147 abstract "Data collected from the field inevitably contains uncertainties such as noise or other disturbances; mathematical models established with data-driven approach must possess strong capability to deal with the influence of uncertainties. Following analysis of current methods for type-I Takagi-Sugeno (T-S) fuzzy modeling,a method suitable for type-Ⅱ T-S fuzzy modeling was proposed. In the data driven modeling process,the influence of the degree of uncertainty was determined by analysis of the collected data. On the basis of the type-I fuzzy model,for antecedent parameters,we employed the fuzzy set of the type-Ⅱ fuzzy model to replace the counterpart from the type-Ⅰ model. But for consequent parameters,we took type-I fuzzy sets to replace crisp numbers. This produced an improved type-Ⅱ T-S fuzzy model. Finally,a pH neutralization process was taken as an example to verify the proposed mathematical model. Simulation results showed that this method can handle the influence of uncertainties better and achieves higher accuracy." @default.
- W2362020147 created "2016-06-24" @default.
- W2362020147 creator A5041126305 @default.
- W2362020147 date "2009-01-01" @default.
- W2362020147 modified "2023-09-22" @default.
- W2362020147 title "A Type-II T-S fuzzy modeling method for data-driven approaches" @default.
- W2362020147 hasPublicationYear "2009" @default.
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