Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/13453
標題: 倒傳遞類神經網路在波浪預報之應用
Back-Propagation Neural Network on Wave Forecast
作者: 朱良瀚
chu, liang-han
關鍵字: Box-Jenkins;倒傳遞類神經網路;neural network;wave forecast;波浪即時預報;時序列分析模式
出版社: 土木工程學系
摘要: 
本文係藉由倒傳遞類神經網路建立波浪即時預報模式,預測單一測站波浪
時序列變化特性,並於模式架構上類比Box-Jenkins時序列分析模式之輸
出入關係,以提高預測精度。本文首先以Bretschneider波譜之試驗波浪
時序列探討倒傳遞類神經網路中不同演算法則及網路非線性輸出單元架構
對精度之影響。本研究並以台中港及高雄LNG港之波浪實測資料進行實例
操作,驗證模式之可行性;以冬季風浪及夏季颱風分別進行模式之波浪即
時預報,結果顯示,在冬季風浪期間,不論以單一月份或多月份實例條件
訓練模式中之連接權值,皆有良好之預測表現;而在夏季颱風季節之預報
中,發現使用較長之波浪實例資料用以檢定模式之預測較佳。

In this paper, the back-propagation neural network (BPN)
associated the I/O relationship in the Box-Jenkins model
isestablished for the wave forecast model. The virtue of
artificialneural network model is available for the short-term
time series, thus it is useful for the wave prediction of
offshore and coastal regions. A time series of Bretschneider
wave spectrum performed in the laboratory is firstly adopted in
this study to optimize the algorithm and network topology of the
back-propagation neural network. The site wave data measured at
Taichung Harbor and Kaoshiung LNG Port are then used to verify
the accuracy of the model, based on the analysis of the
efficiency coefficient, correlation coefficient and the root
mean squared error between predicted and observed data. Waves of
winter type and summer type are respectively simulated in the
verification of model. The results show that the prediction has
good performance in the winter waves when the short or longer
training data is used. However, a longer training data should be
utilized to have better performance for the summer waves due to
storm waves being involved in the season.
URI: http://hdl.handle.net/11455/13453
Appears in Collections:土木工程學系所

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