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碩士論文公告區 |
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| 年度： | 96 | |
| 姓名： | 張瑜倫(Yu-lun Chang) | |
| 論文題目(中)： | 以類神經網路模擬受拉力作用下冷軋型角鋼之行為分析研究 | |
| 論文題目(英)： | The Study of Neural Network Simulation on Bolted L-Shaped Cold-Formed Steel Tension Members | |
| 摘要(中)： | ||
| 本研究主要乃針對冷軋型角鋼構件受拉強度的探討，利用過去研究所得的資料，以類神經網路應用來預測構件的受拉強度；冷軋型鋼角鋼斷面的實驗結果主要被應用為本研究的分析母體，另外冷軋型鋼槽型斷面也被同時證實可利用類神經網路預測結果。 本研究採用的是倒傳遞類神經網路，其中一共包含三個層，分別是輸入層、隱藏層與輸出層。利用Matlab的NN toolbox，BPN結構，輸入層共有4個神經元的輸入參數，輸出層為一個輸出變數。輸入層選以下四種與強度有關的參數，即Wu/Wc、 /L、An、Fu，以此作為ANN之輸入參數，輸出層由則只有一個輸出變數 Pult。 雖然類神經網路不具有物理模型，但對影響因素複雜且具高度非線性與不確定性的冷軋型鋼角鋼極限承載力Pult問題，透過適當的參數選取與網路架構建立，仍可求得輸入與輸出間複雜的函數映射關係，而獲得相當令人滿意的結果。 |
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| 摘要(英)： | ||
| This research is concentrated on the tensile strength of cold-formed steel members. By using the experimental data from the previous studies, the Artificial Neural Networks simulation was adopted for the prediction of thesile strength. The tested ultimate strengths of cold-formed steel angle sections were mainly used in the application of analysis, and the tested values of cold-formed steel channel sections were also utilized to verify the prediction of proposed process. The process of Back Propagation Network was selected for analysis. This network consists of three layers, input layer, hidden layer, and output layer. By applying the Matlab NN toolbox BPN structure, input layer has four neurons for input parameters, and output layer has only one output variable. It was found that Wu/Wc、 /L、An and Fu, these four input parameters provided better prediction as compared to the target values (tested ultimate strengths). The output variable is assigned to be Pult, as expected. Although the solution of Artificial Neural Networks does not have a physical model, neural network process can deal with the problem with complexity and nonlinearity characteristics. By applying the appropriate parameter inputting and network structure, the output can map the real world results very well. It was found that the predictions computed by using neural network can make a good agreement with the tested strengths of cold-formed steel tension members in this study. |
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| 檔案： | 沒有相關檔案 | |
| 相關連結： | http://ethesys.lib.cyut.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0830108-012937 | |