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Tide Forecasting and Supplement by applying Wavelet Theory and Neural Network

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 .

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0720101-172356
Date20 July 2001
CreatorsWang, H.D
ContributorsJ.R.C Hsu, Chia-Chuen Kao, Bang-Fhu Chen
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0720101-172356
Rightsoff_campus_withheld, Copyright information available at source archive

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