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铁道科学与工程学报

JOURNAL OF RAILWAY SCIENCE AND ENGINEERING

Vol. 11    No. 2    April 2001

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Artificial neural network model of constitutive relations
for shock-prestrained copper
YANG Yang(杨 扬), ZHU Yuan-zhi(朱远志), LI Zheng-hua(李正华),
ZHANG Xin-min(张新明), YANG Li-bin(杨立斌), CHEN Zhi-yong(陈志永)

Department of Material Science and Engineering,
Central South University, Changsha 410083, P.R.China

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%.

 

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

ISSN 1672-7029
CN 43-1423/U

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