人工神经网络预测屈服强度  

Application of Artificial Networks in Prediction of Yield Strength

作　　者：侯哲哲[1] 张筠[1] 

机构地区：[1]石家庄铁道学院材料科学与工程分院,河北石家庄050043

出　　处：《热加工工艺》2006年第10期58-59,共2页Hot Working Technology

摘　　要：在实验数据的基础上,采用附加动量项和变步长的方法,对人工神经网络的BP算法进行了训练。利用训练后所得到的模型,对屈服强度进行了分析和预测。计算表明,网络预测值与实测值之间具有很高的相关性和精确度,为屈服强度提供了一定的理论辅助手段。Based on experimental data, the back-propagating algorithm in artificial neural networks （ANNs） was trained by appending momentum and changing steps. According to the trained model, the critical points for yield strength were analyzed and predicted. The results show that the prediction precision and the pertinency between the predicted ANNs and measured values are considerably high. A Theoretical method for prediction martensite start temperature is given,

关 键 词：屈服强度 神经网络 BP算法 

分 类 号：TG153.1[金属学及工艺—热处理]