In multiresolution analysis(MRA)by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency content,respectively. We remove the high-frequency content and reconstruct a new ¡§de-noise¡¨ signal by using inverse wavelet transform. In order to improve the forecasting accuracy of ANN (Artificial Neural Network) model ,we use the concept of tidal constituent phase-lags, and the new ¡§de-noise¡¨ signal was used as the input data set of ANN. Besides, we also use wavelet spectrum, conventional energy spectrum (Fast Fourier Transform, FFT),and harmonic analysis to analyze the character of tidal data .
The results show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and supplement, but in the wavelet analysis , the improvement is insignificant .The reason is that the energy of higher frequency noise is very small compared to the energy of the diurnal and the semi- diurnal tidal components. In other word , the ANN model has a certain tolerance of noise effect .
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0720101-172356 |
| Date | 20 July 2001 |
| Creators | Wang, H.D |
| Contributors | J.R.C Hsu, Chia-Chuen Kao, Bang-Fhu Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0720101-172356 |
| Rights | off_campus_withheld, Copyright information available at source archive |
Page generated in 0.0028 seconds