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Predicting ms temperature applying principal component analysis-artificial neural networks
Xuexia Xu ; Binggzhe Bai ; Wei You
2010-10-12 ; 2010-10-12
关键词Theoretical or Mathematical/ martensitic transformations neural nets principal component analysis steel/ martensite transformation start temperature steels principal component analysis artificial neural network statistical errors FeCJk/ A8130K Martensitic transformations A0250 Probability theory, stochastic processes, and statistics/ Fe/ss C/ss
中文摘要The principal component analysis-artificial neural network (PCA-ANN) model was developed to predict martensite transformation start temperature (Ms) of steels. Training samples were processed by principal component analysis and the number of input variables was reduced from 6 to 4, then the scores of principal components were used to establish new sample database to train the ANN model. Ms of steels were predicted by the PCA-ANN model. The predicted and measured Ms distribute along the 0-45 degrees diagonal in the scatter diagram and the statistical errors are MSE-16.0256, MSRE4.49% and VOF-1.97790 respectively. Comparing the prediction results of different models it is shown that the accuracy of the PCA-ANN model was the highest, which indicated that the principal component analysis was helpful to improve the prediction accuracy of ANN model.
语种英语
出版者World Scientific Publishing Co. Pte. Ltd. ; Singapore
内容类型期刊论文
源URL[http://hdl.handle.net/123456789/78524]  
专题清华大学
推荐引用方式
GB/T 7714
Xuexia Xu,Binggzhe Bai,Wei You. Predicting ms temperature applying principal component analysis-artificial neural networks[J],2010, 2010.
APA Xuexia Xu,Binggzhe Bai,&Wei You.(2010).Predicting ms temperature applying principal component analysis-artificial neural networks..
MLA Xuexia Xu,et al."Predicting ms temperature applying principal component analysis-artificial neural networks".(2010).
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