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 models 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.
Original language | English |
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Article number | FrC04.2 |
Pages (from-to) | 5170-5175 |
Number of pages | 6 |
Journal | Proceedings of the IEEE Conference on Decision and Control |
Volume | 5 |
DOIs | |
Publication status | Published - 2004 |
Externally published | Yes |
Event | 2004 43rd IEEE Conference on Decision and Control (CDC) - Nassau, Bahamas Duration: 14 Dec 2004 → 17 Dec 2004 |