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- W2134175394 abstract "The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved model accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example." @default.
- W2134175394 created "2016-06-24" @default.
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- W2134175394 date "2004-01-01" @default.
- W2134175394 modified "2023-09-24" @default.
- W2134175394 title "Enhanced prediction accuracy of fuzzy models using multiscale estimation" @default.
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- W2134175394 doi "https://doi.org/10.1109/cdc.2004.1429628" @default.
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