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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