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Neural Network Prediction of Conversion Rate of TbFe2 Alloy Prepared by Reduction-Diffusion Process
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Neural Network Prediction of Conversion Rate of TbFe2 Alloy Prepared by Reduction-Diffusion Process
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National Natural Science Foundation of China (51377110)

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    摘要:

    針對還原擴(kuò)散法制備TbFe2合金的主要實驗參數(shù)：反應(yīng)溫度、保溫時間、Ca的加入量及Fe的粒度，建立BP神經(jīng)網(wǎng)絡(luò)，進(jìn)行仿真，預(yù)測TbFe2合金的轉(zhuǎn)化率。以44組實驗數(shù)據(jù)作為訓(xùn)練樣本，進(jìn)行了網(wǎng)絡(luò)設(shè)計。通過測試及對網(wǎng)絡(luò)的性能分析，證明了該網(wǎng)絡(luò)能夠準(zhǔn)確預(yù)測不同實驗參數(shù)下TbFe2合金的轉(zhuǎn)化率，并具有良好的性能。該網(wǎng)絡(luò)的設(shè)計可以縮短實驗周期，節(jié)約實驗成本，并對反應(yīng)的機(jī)理及工藝研究有一定的價值。

    Abstract:

    A BP neural network was established based on the following main experiment parameters of producing TbFe2 alloy by reduction-diffusion process: reaction temperature, holding time, quantity of Ca and particle size of Fe. A simulation was conducted, and the rate of conversion of TbFe2 alloy was predicted. The neural network was simulated and tested by 44 groups of experimental data. It can be concluded that the neural network has good performance to predict the rate of conversion of TbFe2 alloy. The design and the application of this neural network can help to shorten the periodic time of experiments, lower the experimental cost, and optimize the preparation processes.

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郭廣思,王廣太,成永君,胡小媚. Neural Network Prediction of Conversion Rate of TbFe2 Alloy Prepared by Reduction-Diffusion Process[J].稀有金屬材料與工程,2015,44(5):1104~1107.[Guo Guangsi, Wang Guangtai, Cheng Yongjun, Hu Xiaomei. Neural Network Prediction of Conversion Rate of TbFe2 Alloy Prepared by Reduction-Diffusion Process[J]. Rare Metal Materials and Engineering,2015,44(5):1104~1107.]
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  • 收稿日期:2014-05-30
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  • 在線發(fā)布日期: 2015-06-08
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