Artificial neural network model of constitutive relations for shock-prestrained copper

来源期刊：中国有色金属学报(英文版)2001年第2期

论文作者：杨扬 朱远志 李正华 张新明 杨立斌 陈志永

文章页码：210 - 212

Key words：shock-prestrain; constitutive relations; artificial neural network model

Abstract: Data from the deformation on Split-Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5, the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1, and the accuracy of the rate-error is less than 3%.