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hwjok木蟲 (正式寫手)
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[求助]
請幫忙改正摘要翻譯中的錯誤
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The online measurement of Dioxins in the waste incineration is difficult and could only be analyzed with small samples offline. Aimed at the problem, a novel soft sensing methodology with good generalization is studied. Firstly, the small samples are increased with diversity by injecting noise and using the bootstrap resampling approach. Then, the neural network with maximum entropy is presented by introducing information entropy to error rule function for the unknown distributing of original samples. Finally, a soft sensing model of dioxins is built with this entropy neural network. Simulations show that the model has good generalization and precision. The mean and maxim values of relative error between true and prediction of dioxins is 0.167% and 1.21%, respectively. It provides a referenced method for detecting dioxins online in the waste to energy. 垃圾焚燒過程中的二惡英難以在線測量, 只能通過離線分析獲得少量樣本. 針對該問題, 研究了一種在小樣本條件下仍然具有推廣能力的軟測量建模方法. 首先對小樣本進行Bootstrap重抽樣和噪聲注入處理, 增加樣本的數量和改善其多樣性. 然后將信息熵引入誤差準則函數, 構建出最大熵神經網絡. 最后基于熵神經網絡建立二惡英軟測量回歸模型. 仿真結果表明, 該二惡英軟測量模型具有較好的精度和泛化能力, 實際值和預測值的相對誤差均值為0.167%, 最大相對誤差為1.21%, 為在線測量垃圾焚燒發(fā)電過程中的二惡英提供了一種參考方法. [ Last edited by hwjok on 2013-5-19 at 22:56 ] |
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yili_90
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hwjok
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yili_90
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yili_90
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6樓2013-05-20 18:00:34
bhcsu
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hwjok: 金幣+30, 翻譯EPI+1, ★★★很有幫助 2013-05-21 22:36:13
hwjok: 金幣+30, 翻譯EPI+1, ★★★很有幫助 2013-05-21 22:36:13
| Since the online measurement of Dioxins during waste incineration is difficult, it could only be analyzed offline with small samples obtained. Aimed at this problem, a novel soft sensing methodology that can be well generalized is studied. Firstly, bootstrap resampling approach and noise injection are performed for small samples in order to increase the amount of the samples and improve the diversity. Then, the information entropy is introduced to the error rule function for the unknown distributing of original samples and construct a neural network with the maximum entropy. Finally, a soft sensing regression model of dioxins is built based on the entropy neural network. Simulation results show that this model has a high precision and a good ability of generalization. The mean and maximum of relative error between actual and predicted values are 0.167% and 1.21%, respectively. This method provides a reference for detecting dioxins online during incinaerating waste. |
7樓2013-05-21 20:11:51
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