Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/15460
標題: 以徑向基函數類神經網路預測暴風型海灘斷面特性
Prediction of Storm-Built Beach Profile Using Radial Basis Function Artificial Neural Network
作者: 黃正同
Huang, Cheng-Tung
關鍵字: RBFN
徑向基函數類神經網路
出版社: 土木工程學系所
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摘要: 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。
This 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..
URI: http://hdl.handle.net/11455/15460
其他識別: U0005-3108200616140900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-3108200616140900
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