PREDICTING Ms TEMPERATURE APPLYING PRINCIPAL COMPONENT ANALYSIS-ARTIFICIAL NEURAL NETWORKS

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° diagonal in the scatter diagram and the statistical errors are MSE-16.0256, MSRE-4.49％ 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.

关键词： Martensite start temperature principal component analysis artificial neural network

作者: XUEXIA XU BINGZHE BAI WEI YOU

作者单位: Department of Materials Science and Engineering, Tsinghua University, Beijing, 100084, Peoples Repu Department of Materials Science and Engineering, Tsinghua University, Beijing, 100084, Peoples Repu Department of Mechanics and Electricity Engineering, North China Institute of Science and Technology

会议类型: 国际会议

会议名称: 第五届先进材料与加工国际会议(Fifth International Conference on Advanced Materials and Processing ICAMP-5)

会议地点: 哈尔滨

会议语种:英文

页码: 1099-1104

在线出版日期: 2008-09-03（万方平台首次上网日期，不代表论文的发表时间）