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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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