In maltiresolution analysis （MRA） by wavelet function Daubechies （db）, we decompose the signal to two parts, the low and high frequency content. The high-frequency content of the data is removed first and a new ＂...In maltiresolution analysis （MRA） by wavelet function Daubechies （db）, we decompose the signal to two parts, the low and high frequency content. The high-frequency content of the data is removed first and a new ＂de-noise＂ signal is reconstructed by using inverse wavelet transform. The wavelet spectrum and harmonic analysis were used to analyze the characteristics of tidal data before constructing the input and output structure of ANN model. That is, the concept of tidal constituent phase-lags was introduced and the new ＂de-noise＂ signal was used as the input data set of ANN and the forecasting accuracy of ANN model is significantly improved.展开更多
基金This work was financially supported by the Science Council of Taiwan(Grant No.NSC90-2611-M-110-012)
文摘In maltiresolution analysis （MRA） by wavelet function Daubechies （db）, we decompose the signal to two parts, the low and high frequency content. The high-frequency content of the data is removed first and a new ＂de-noise＂ signal is reconstructed by using inverse wavelet transform. The wavelet spectrum and harmonic analysis were used to analyze the characteristics of tidal data before constructing the input and output structure of ANN model. That is, the concept of tidal constituent phase-lags was introduced and the new ＂de-noise＂ signal was used as the input data set of ANN and the forecasting accuracy of ANN model is significantly improved.
