臺灣博碩士論文加值系統

本文係藉由倒傳遞類神經網路將不同測站的波浪資料分別設為網路
中的輸出 入，經由倒傳遞類神經網路的學習而擬合出一簡便可行的波浪
推算模式，而加以 應用於波浪資料之補遺及即時預報上。本研究係以台
中港ST.1、ST.2以及ST.4三 個波浪觀測站之波浪實測資料，分成多個範
例進行實例操作，以驗證模式之可行 性。模式分析主要以兩測站資料來
推求另一測站資料，另外也對單一測站推求單 一測站的特殊狀況作探討
；結果發現前者之模擬推算效果較後者為佳。此外，在 波高的推算上，
若以資料完整齊全的月份作為訓練，網路較能迅速確實地學習出 權重，
而有很好的推算成果。而在波浪週期的推算上，亦有良好之結果，但成效
較不如波高推算來的理想，此乃由於週期規律性較低，網路較不易學習。

Accurate prediction for the wave climate is an essential part
in the ocean engineering. This paper reports an application of
the artificial neural network for accurate forecast of waves
from a time series of the field data. This paper also presents
the wave supplementary for a wave record using the neural
network model. The back-propagation procedures for minimizing
the error of the desired output is used in the learning process
of the neural network. The field data measured in three wave
stations at Taichung Harbor were used to test theperformance of
the neural network model. The wave prediction from the time
series of one wave stationor two wave stations is presented. It
is found that the neural network model performs well for the
waveforecast and wave supplementary when a very short-term wave
data is used as a training set. In general, the wave prediction
or wave supplementaryfrom the time series of two wave stations
has betterperformance than one wave station records used. It is
also found that the performance of theprediction of significant
wave heights isbetter than that of wave periods.