摘要
本文首次利用神经网络对Cu Cr Zr合金时效温度和时间与硬度和导电率样本集进行学习 ,采用改进的BP网络算法———Levenberg Marquardt算法 ,建立了时效强化工艺BP神经网络模型。预测结果表明 :该BP神经网络可以充分挖掘样本蕴含的领域知识 。
The paper proposes the use of a supervised artificial neural network (ANN) to model the non-linear relationship between parameters of age hardening processes and hardness and conductivity properties of CuCrZr alloy. The improved model is developed for the first time by the levenberg- Marquardt training algorithm. A basic repository on the domain knowledge of age hardening processes is established via sufficient data mining by the network. The results show that the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Zr alloy.
出处
《材料科学与工程学报》
CAS
CSCD
北大核心
2003年第3期383-386,共4页
Journal of Materials Science and Engineering
基金
河南省重大科技攻关资助项目(0 1 2 2 0 2 1 30 0 )
