臺灣博碩士論文加值系統

即時且準確的風浪推算在海洋工程中可謂第一要務，但由於風浪之間存在著許多非線性的不確定因素，故本文應用倒傳遞類神經網路建立簡便可行的風浪推算模式。類神經網路模式首先經由訓練測試資料的學習，而獲得風速與示性波高之網路連結權重值，再由新的風速輸入資料進行示性波高的預測推算。本研究以太平洋深海測站之風浪資料進行測試評估；首先探討最佳之風浪推算模式的網路架構，再評估適當之訓練天數與預測天數。本文最後決定之適當風浪推算模式為輸入前12小時的風速及波高資料作訓練，進而推算示性波高值，而由28天的風速及波高資料，可推算後7天的預測示性波高對。在示性波高的推算上，除了風速較大及資料完整性較差的月份，其他月份之網路皆能確實迅速地學習出權重而有好的推算成效。

第一章 前言
1-1 研究動機及目的
1-2 文獻回顧
1-2-1 有關風浪推算的文獻
1-2-2 有關類神經網路的文獻
1-3 本文組織
第二章 理論分析
2-1 類神經網路簡介
2-1-1 類神經網路發展史
2-1-2 類神經網路定義
2-1-3 類神經網路基本架構
2-1-4 類神經網路特性
2-2 倒傳遞類神經網路
2-2-1 倒傳遞類神經網路架構
2-2-2 倒傳遞網路演算法
2-2-3 倒傳遞網路參數
2-2-4 倒傳遞網路演算流程
第三章 實例操作
3-1 現場資料之蒐集與分析
3-1-1 深海測站相關資料
3-1-2 現場資料之檢驗與選取
3-1-3 現場資料之預處理
3-2 網路效能評鑑指標
3-3 風浪推算模式之網路架構探討
3-3-1 網路參數之決定
3-3-2 網路訓練條件評估
3-3-3 網路推算條件評估
3-3-4 風浪推算模式之最佳網路架構
3-4 研究範例之編排
3-5 研究範例結果與分析
3-5-1 波高推算之分析
3-5-2 週期推算之分析
第四章 結論與建議
4-1 結論
4-2 建議
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