臺灣博碩士論文加值系統

本研究藉由倒傳遞類神經網路建立風浪預測模式，網路模式係由風浪紀錄資料的輸入，藉由類神經網路的學習過程獲得風速與示性波高間之網路權重，再由新的風速資料輸入後預測推算示性波高。本研究以太平洋深海測站資料進行實例操作以驗證模式之可行性；模式的建立，首先探討網路架構，其次評估訓練天數與預測天數對網路預測效能之影響，最後建立較佳之網路風浪推算架構。本文網路輸入架構，考慮風與浪之間的延遲效應，由20天之訓練天數，當輸入前期18小時的風速及示性波高資料對，即可推算往後5至15天之平均示性波高及最大示性波高。

Due to the complexity and randomness of the wind waves, the prediction of waves based on the simplified relationship may obtain substantial error. This study attempts to predict the wind waves by applying the technique of an artificial neural network (ANN), in which the supervised learning models with back-propagation scheme is adopted. The ANN estimates the interconnection weights between the waves and the corresponding wind speeds by training the past records. The topology of the selection of the neurons of input, outputs and the hidden layers is discussed in detail in the study. Using long-term wind and wave data from one of the National Data Buoy Center (NDBC) buoy stations in the Pacific Ocean, this study examined the validity and accuracy of the ANN model. It is found that the six-hourly average significant wave height and the six-hourly maximum significant wave height can be predicted from training the previous eighteen hours data of waves and wind speeds. The results show that the neural network model can well predict a five-days to fifteen-days of waves using a previous 20-days’ records.

摘要
ABSTRACT
目錄
圖表目錄
符號說明
第一章 前言
第二章 理論分析
2-1 類神經網路簡介
2-2 類神經網路發展史
2-3類神經網路之分類
2-4 倒傳遞類神經網路
2-5 倒傳遞網路演算法
2-6 倒傳遞網路演算流程
第三章 實例操作
3-1 資料來源
3-1-1資料的選取
3-1-2 資料前處理
3-2 模擬效能評鑑指標
3-3風浪推算網路架構之探討
3-3-1網路架構之選取
3-3-2網路參數之選擇
3-3-3風浪推算模式之網路架構
3-4 推測模式結果與分析
第四章 結論與建議
4-1 結論
4-2 建議
參考文獻

英文部分
1. Chakraborty, K., K. Mehrotra, C. K. Mohan and S. Ranka （1992）“Forecasting the Behavior of Multivariate Time Series Using Neural Networks,” Neural Networks, Vol. 5, pp. 961-970.
2. Deo, M. C. and C. Sridhar Naidu (1999) “Real time wave forecasting using neural networks,” Ocean Engineering 26, 191－203.
3. Deo, M. C., A., Jha, A. S. Chaphekar, K. Ravikant (2001) “Neural networks for wave forcasting,” Ocean Engineering 28, 889－898.
4. Ensley, D. and D. E., Nelson (1992) “Extrapolation of Mackey-Glass Data Using Cascade Correlation,” Simulation, Vol. 58, NO5, pp.333-339.
5. Hebb, D. O. (1949) “The Organization of Behavior. A Neuropsy-chological Theory,” New York: John Wiley.
6. Hopfield, J. J. (1982) “Neural Networks and Physical Systems with Emergent Collective Computional Abilities,” Porc. Natl. Academy Sci., Vol. 79, pp.2554-2558.
7. Hopfield, J. J. and D. Tank (1985) “Neural Computation of Decision in Optimization Problems,” Biological Cybernetics, Vol. 51, pp.141-152.
8. Ippen, A. T. (1966) “Estuary and Coastline Hydrody namics,” McGraw-Hill, New York.
9. Jacobs, R. A. (1988) “Increased Rates of Convergence Through Learning Rate Adaptation,” Neural Network, Vol. 1, pp.295-307.
10. McCulloch, W. S. and W. H. Pitts (1943) “A logical Calculus of the Ideas Imminent in Nervous Activity,” Bull. Math. Biophysics, Vol. 5, pp.115-123.
11. Minsky, M. and S. Papert (1969) Perceptrons, MIT press., Cambridge, Mass.
12. Rosenblatt, F. (1958) “The Perceptron: A Probabilistic Model for Information Storage and Organization,” Psych., Rev., Vol. 65, pp.386-408.
13. Rumelhart, D. E., J. L. McClelland and PDP Research Group (1986)
Parallel Distributed Processing Vol. 1, MIT Press.
14. Smith, M., “Neural Network for Statistical Modeling,”Van
Norstrand Reinhold, New York, 1993.
15. Tang, Z., C. D. Almeida and P. A. Fishwick (1991) “Time series forcasting using neural networks vs. Box-Jenkins methodology,” Simulation, pp.303-310.
16. Tsai, C. P. and T. L. Lee (1999) “Back-Propagation Neural Network in Tidal-Level Forecasting,” Journal of Waterway, Port, Coastal, and Ocean Engineering., ASCE, Vol. 125, NO. 4, pp.188-195.
17. Tsai, C. P., C. Lin and J. N. Shen (2002) “Neural network for wave forecasting among multi-stations,” Ocean Engineering 29, pp.1683-1695.
18. Villiers, J. and E. Barnard (1992) “Back-Propagation Neural Nets with One and Two Hidden Layers,” IEEE Transaction On Neural Network, Vol. 4, NO. 1, pp.136-141.
19. Werbos, P. J. (1974) “Beyond Regression: New Tools for Predictionand Analysis in the Behavioral Sciences,” Doctoral Dissertation, Appl. Math., Harvard University, Mass.
20. WMO (1998), Guide To Wave Analysis and Forcasting, Secretariat
of the World Meteorological Organization, Geneva, No. 702.
中文部分
21. 郭金棟（1976），「台中港區波浪預測方法之研究」，中興大學土木係研究報告第四號。
22. 高治平、梁乃匡（1980），「台灣北部海域冬季波候研究」，第四
屆海洋工程研討會論文集，第245-258頁。
23. 侯和雄、劉正忠（1982），「台灣海峽風浪特性之研究」，第六屆
海洋工程研討會論文集，第8之1-18頁。
24. 莊甲子、吳 基、周哲民（1992），「台灣北部海岸風浪特性之研
究」，第十四屆海洋工程研討會論文集，第92-106頁。
25. 葉怡成（1993），「類神經網路模式應用與實作」，儒林。
26. 蔡清標、李宗霖、朱良翰（1999），「倒傳遞類神經網路在波浪時序列預報之應用」，中國土木水利工程學刊，第十一卷，第三期，第175-182頁。
27. 謝榮哲，「類神經網路在長時期潮汐預報之應用」，碩士論文，國
立中興大學土木工程學系，台中（2000）。
28. 蔡金晃，「類神經網路在風浪推算的應用研究」，碩士論文，國立中興大學土木工程學系，台中（2001）。