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- W323776536 abstract "Abstract This paper presents a methodology for the identification of dynamic models using fuzzy logic that considers the inclusion of the measurement uncertainty in the process variables (described by the probability density function) directly in the model structure. The model comprises a type-2 fuzzy inference system, based on Takagi-Sugeno, together with the use of inputs and outputs in the non-singleton form (represented by membership functions) in both antecedent and consequent of each rule. Transformations of possibility measures into probability measures and conversely were also considered. A case study comprising a CSTR was analyzed and the results predicted by the fuzzy model were compared with Monte Carlo simulation. The proposed method presented good performance and provides a potential way to predict the dynamic propagation of measurement uncertainty through the process." @default.
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- W323776536 date "2012-01-01" @default.
- W323776536 modified "2023-10-18" @default.
- W323776536 title "Dynamic Model Identification with Uncertain Process Variables using Fuzzy Inference System" @default.
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- W323776536 doi "https://doi.org/10.1016/b978-0-444-59506-5.50022-5" @default.
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