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- W2158962365 abstract "This paper presents an approach for learning the Takagi-Sugeno (T-S) fuzzy model by Genetic Algorithm (GA). In this approach, the fuzzy rule structure is encoded by binary code in the chromosome in which the position of 1 indicates the selected rules and the sum of 1 indicates the number of rules. The membership function (MF) parameters (centres and bases) are evolved by GA in combining with the pseudo-inversion algorithm for obtaining the consequent parameters. The sum of squared error (SSE) between the true output and the T-S model prediction is used as objective function. Then, this approach is applied to the modelling of dynamic behaviour of a magneto-rheological (MR) damper which shows highly nonlinear characteristics due to hysteretic phenomenon. It is shown by the validation test that the developed T-S fuzzy model can represent the dynamic behaviour of the MR damper satisfactorily." @default.
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- W2158962365 date "2006-12-13" @default.
- W2158962365 modified "2023-09-22" @default.
- W2158962365 title "Evolutionary Takagi-Sugeno Fuzzy Modelling for MR Damper" @default.
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- W2158962365 doi "https://doi.org/10.1109/his.2006.25" @default.
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