Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/15460
DC FieldValueLanguage
dc.contributor林朝福zh_TW
dc.contributor陳文俊zh_TW
dc.contributor莊甲子zh_TW
dc.contributor.advisor蔡清標zh_TW
dc.contributor.author黃正同zh_TW
dc.contributor.authorHuang, Cheng-Tungen_US
dc.contributor.other中興大學zh_TW
dc.date2007zh_TW
dc.date.accessioned2014-06-06T06:54:03Z-
dc.date.available2014-06-06T06:54:03Z-
dc.identifierU0005-3108200616140900zh_TW
dc.identifier.citation參考文獻 1. Bruun, P., “Coast Erosion and Development of Beach Profiles,” Technical Memorandum, No. 44, Beach Erosion Board. U.S. Army Corps of Engineers, Vicksburg, 1954 2. Dean, R.G., “Equilibrium Beach Profiles: U.S. Atlantic and Gulf Coasts”, department of Civil Engineering, Ocean Engineering Report No.12, University of Delaware, Newark, DE, 1977 3. Ensley, D. and Nelson, D. E.,“Extrapolation of Mackey-Glass Data Using Cascade Correlation,”Simulation, Vol. 58, No. 5, pp. 333-339, 1992. 4. French, M.N., Krajewski, W.F. and Cuykendall. R.R.,“Rainfall forecasting in space and time using a neural network” Journal of Hydrology, vol. 137, pp. 1-31, 1992. 5. Hsu, T.W. and Wang, H.,“Geometric Characteristics of Storm Beach Profiles,” Journal of Coastal Research, Vol. 13, No. 4, pp 1102-1110, 1997 6. Hebb, D. O., The Organization of Behavior, A Neuropsy-chological Theory, New York: John Wiley, 1949. 7. Hopfield, J. J., “Neural Networks and Physical Systems with Emergent Collective Computional Abilities,”Porc. Natl. Academy Sci., Vol.79, pp. 2554-2558, 1982. 8. Hopfield, J. J. and Tank, D.,“Neural Computation of Decision in Optimization Problems,”Biological Cybernetics, Vol.51, pp.141-152, 1985. 9. Jacobs, R. A.,“Increased Rates of Convergence Through Learning Rate Adaptation,”Neural Network, Vol. 1, pp.295-307, 1988. 10. J. S. Chen, “Fast calibration and modeling of thermally-induced machine tool errors in real machining,” International Journal Machine Tools Manufacturing, Vol.37, No. 2, 159-169, 1996. 11. Komar, P.D. and McDougal W.G, “The Analysis of Exponential Beach Profiles,” Journal of Coastal Research, Vol. 10, No. 1, pp. 59-69, 1994 12. Larson, M., Kraus N.C. and Byrnese M.R., “SBEACH : Numerical model for simulating Storm-induced beach Change, Rep-2;Numerical formulation and model tests,” U.S. Army Corps of Engrs., Coastal Eng. Res. Center, Waterways Exp. Sth.., Vicksburg, Miss., Tech. Rep. CERE-89-8. 1989 13. McCulloch, W. S. and Pitts., W. H.,“A logical Calculus of the Ideas Imminent in Nervous Activity,”Bull. Math. Biophysics, Vol. 5, pp. 115-123, 1943. 14. M. D. Powell, “Radial basis functions for multivariable interpolation: A review”, In Algorithms for Approximation. Oxford University Press, pp.143-167, 1987. 15. Minsky, M. and Papert, S., Perceptrons, MIT press., Cambridge, Mass., 1969. 16. Mase, M., Sakamoto, M., and Sakai, M., “Neural Network for Stability Analysis of Rubble-Mound Breakwaters,” Journal of Waterway, Port, Coastal, and Ocean Engineering. Vol. 121, No. 6, 1995. 17. Rosenblatt, F.,“The Perceptron: A Probabilistic Model for Information Storage and Organization,”Psych., Rev., Vol. 65, pp.386-408, 1958. 18. Smith, M.,“Neural Networks for Statistical Modeling,”Van Norstrand Reinhold, New York, 1993. 19. Silvester, R. and Hsu J.R.C. “Coastal Stabilization, Innovative Concepts,“Prenfice Hall, Ice., Englewood Cliffs, New Jersey., pp. 107-160, 1993 20. Tsai, C. P. and Lee, T. L., “Back-Propagation Neural Network in Tidal-Level Forecasting,”Journal of Waterway, Port, Coastal, and Ocean Engineering., Vol. 125, No. 4, pp. 188-195, 1999. 21. Tsai, C.P., Hsu, J.R.C. and Pan, K.L., “Prediction of storm-built beach profile parameters using neural network,” Proceedings 27th International Conference on Coastal Engineering.,ASCE, Vol. 2, pp. 3048-3060, 2000 22. Tsai, C.P., Lin, C. and Shen, J.N., “Neural Network for Wave Forecasting among Multi-Stations,” Ocean Engineering, Vol. 29, No. 13, pp. 1683 -1695, 2002 23. Villiers J. and Barnard E., “Back-Propagation Neural Nets with One and Two Hidden Layers,”IEEE Transaction On Neural Network, Vol. 4, No. 1, pp. 136-141, 1992. 24. Werbos, P. J.,“Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,”Doctoral Dissertation, Appl. Math., Harvard University, Mass., 1974. 21.蔡清標、李宗霖、朱良瀚 (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。 22.林淑貞,李宗仰,「神經元傳輸函數在水文時序建模之分析」,第八屆水利工程研討會,第211-218頁,1996。 23.許泰文、廖建明、林政毅 “暴風型海灘平衡剖面預測模式研究”,中國土木水利學刊,1997。 24.孫建平,「類神經網路及其應用於降雨-逕流過程之研究」,第十四屆海洋工程研討會論文集,第1297-1308頁,1992。 25.葉怡成,「類神經網路模式應用與實作」,儒林,第69-100頁,1993。 26.劉新達,「類神經網路在水庫操作的應用」,碩士論文,國立交通大學土木工程研究所,1995。 27.駱國陽,「類神經網路在結構系統辯識上之應用」,碩士論文,國立交通大學土木工程研究所,1993。 28.趙育漢,「以類神經網路分析微影覆蓋幾何誤差」,碩士論文,私立中原大學機械工程學系研究所,2003。 29.林大偉,「混凝土火害後強度折減之徑向基類神經網路分析」,碩 士論文,私立朝陽科技大學營建工程學系研究所,2004。 30.楊志斌,「類神經網路在兩測站間波浪資料互補之應」,碩士論文 ,國立中興大學土木工程研究所,2004。zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/15460-
dc.description.abstract本文旨在以徑向基底函數類神經網路(RBFN)建立暴風型海灘斷面之預測模式。本文以前人利用大型波浪試驗水槽實測所得之18組暴風型海灘斷面實驗資料來作為RBFN的學習,藉學習過程調整各神經元相關之連接權重,使預測值和實際值的誤差達到最小化,同時求得之權重進行演算,在RBFN架構下輸入無因次後的波浪資料如深海波高、底質粒徑及前灘坡度等參數即可預測暴風型海灘斷面重要之物理參數。由於RBFN有對未知非線性函數近似的能力,研究顯示於暴風型海灘斷面重要之物理參數預測上,與前人回歸分析和倒傳遞類神經網路比較的結果,本模式的預測效果可得最佳之準確度。zh_TW
dc.description.abstractThis study aims to investigate the applicability of the Radial-Basis Function neural network (RBFN) for predicting the major pertinent parameters of a storm-built beach profile. The prediction model is performed from learning 18 model bar profiles selected from previous large wave tank test. A Radial-Basis Function network procedure was used to adjust the weights of the connections in the neural network and to minimize the error between the desired outputs and the observed values. Base on the proposed RBFN model that it has curve fitting capability, the major geometric parameters for a storm-built bar are predicted well as the nondimensional wave condition is given. The results show that the neural network model works better then the previous empirical predictions of Silvester and Hsu (1993) and back-propagation neural network..en_US
dc.description.tableofcontents目 錄 摘要 I ABSTRACT II 目 錄 III 表目錄 V 圖目錄 VI 第一章 前言 1 1-1研究之動機及目的 1 1-2文獻回顧 2 1-2-1 暴風型海灘方面 2 1-2-2 類神經網路方面 4 第二章 理論分析 6 2-1 類神經網路簡介 6 2-1-1 類神經網路發展史 6 2-1-2 類神經網路定義 8 2-1-3 類神經網路基本架構 8 2-1-4 類神經網路特性 10 2-2 徑向基底函數類神經網路 10 2-2-1徑向基底函數類神經網路架構 11 2-2-2 徑向基底函數類神經網路演算法 12 2-3-3 RBFN預測效能評鑑指標 16 2-2-4 徑向基底函數類神經網路演算流程 17 三、實例操作 19 3-1資料來源 19 3-1-1資料前處理 19 3-2暴風型海灘斷面特性各參數分析 21 3-3 RBFN及BPN網路模式之評估 21 3-4 有因次RBFN及BPN網路架構之討論 24 3-5 無因次RBFN及BPN網路架構之討論 27 3-6 暴風型海灘各參數之預測結果 30 3-7 敏感度分析 32 四、結論與建議 34 4-1 結論 34 4-2 建議 35 參考文獻 36 表目錄 表3-1 暴風型海灘地形實驗資料(SILVESTER & HSU,1993) 40 表3-2 沙洲頂點距離(XC)預測之C.C.值 41 表3-3 沙洲體積(VBAR)預測之C.C.值 41 表3-4 沙洲頂點距離XC/L0預測之C.C.值= 42 表3-5 沙洲體積VBAR/H0L0預測之C.C.值 42 表3-6 RBFN網路最佳化之模式 43 表3-7 BPN網路最佳化之模式 43 表3-8暴風型海灘無因次下各參數預測之相關係數(C.C.)值 44 表3-9 暴風型海灘有因次下各參數預測之相關係數(C.C.)值 44 表3-10 RBFN網路模式中輸入無因次參數對預測參數之敏感度 44 圖目錄 圖1-1 正常型海灘示意 45 圖1-2 暴風型海灘示意圖 45 圖2-1 人工神經元模型 46 圖2-2 高斯函數 46 圖2-3 RBFN結構圖 47 圖2-4 訓練停止機制 48 圖2-5 RBFN訓練流程 49 圖3-1 沙洲頂點水深(HC/LO)預測之散佈圖 50 圖3-2 沙洲頂點距離(XC/LO)預測之散佈圖 51 圖3-3 沙洲平衡點水深(HE/LO)預測之散佈圖 52 圖3-4 沙洲平衡點距離(XE/LO)預測之散佈圖 53 圖3-5沙洲體積(VBAR )預測之散佈圖 54 圖3-6 沙洲頂點水深(HC)預測之散佈圖 55 圖3-7 沙洲頂點距離(XC)預測之散佈圖 56 圖3-8 沙洲平衡點水深(HE)預測之散佈圖 57 圖3-9 沙洲平衡點距離(XE)預測之散佈圖 58 圖3-10 沙洲體積(VBA)預測之散佈圖 59zh_TW
dc.language.isoen_USzh_TW
dc.publisher土木工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-3108200616140900en_US
dc.subjectRBFNen_US
dc.subject徑向基函數類神經網路zh_TW
dc.title以徑向基函數類神經網路預測暴風型海灘斷面特性zh_TW
dc.titlePrediction of Storm-Built Beach Profile Using Radial Basis Function Artificial Neural Networken_US
dc.typeThesis and Dissertationzh_TW
item.grantfulltextnone-
item.openairetypeThesis and Dissertation-
item.languageiso639-1en_US-
item.fulltextno fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:土木工程學系所
Show simple item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.