Enhanced prediction accuracy of fuzzy models using multiscale estimation
Mohamed Numan Nounou, Hazem Numan Nounou · 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) · 2004
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.