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神經(jīng)網(wǎng)絡(luò)預(yù)測還原擴散法制備DyFe2合金轉(zhuǎn)化率的研究
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TP183

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遼寧省博士啟動基金（20021021）


Neural Network Prediction of Transformation Efficiency of DyFe2 Alloy Prepared by Reduction-Diffusion Process
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    摘要:

    針對還原擴散法制備DyFe2合金中的主要實驗參數(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)，進行仿真，預(yù)測DyFe2合金的轉(zhuǎn)化率。以44組實驗數(shù)據(jù)作為訓(xùn)練樣本，進行網(wǎng)絡(luò)設(shè)計，并對網(wǎng)絡(luò)進行了測試。證明該網(wǎng)絡(luò)能夠預(yù)測不同實驗參數(shù)下DyFe2合金的轉(zhuǎn)化率，且具有良好的性能。該網(wǎng)絡(luò)的設(shè)計可以縮短實驗周期，降低實驗成本，并有利于工藝優(yōu)化。

    Abstract:

    Based on the main experiment parameters of DyFe2 alloy preparation by reduction-diffusion process: reaction temperature, holding time, added quantity of Ca and particle size of Fe, the BP neural network was established and used to predicate the transformation efficiency of DyFe2 alloy. The neural network was simulated by 44 groups of experimental data and was tested. It has been proved that the neural network has good performance to predict the transformation efficiency of DyFe2 alloy. This design of neural network is able to shorten the time of experiment, reduce the experiment cost, and optimize the preparation processes.

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郭廣思 成永君 胡小媚 葉飛.神經(jīng)網(wǎng)絡(luò)預(yù)測還原擴散法制備DyFe2合金轉(zhuǎn)化率的研究[J].稀有金屬材料與工程,2007,36(4):721~723.[Guo Guangsi, Cheng Yongjun, Hu Xiaomei, Ye Fei. Neural Network Prediction of Transformation Efficiency of DyFe2 Alloy Prepared by Reduction-Diffusion Process[J]. Rare Metal Materials and Engineering,2007,36(4):721~723.]
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  • 最后修改日期:2006-09-18
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