基于人工神经网络的铜合金时效性能研究

苏娟华; 李贺军; 董企铭; 刘平; 康布熙

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基于人工神经网络的铜合金时效性能研究
Effects of Age hardening on the Properties of Cu Alloy Based on An Artificial Neural Network（ANN）
2003年第5期页码：29-31
纸质出版日期： 2003 ，
DOI：

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[1]苏娟华,李贺军,董企铭,刘平,康布熙.基于人工神经网络的铜合金时效性能研究[J].特种铸造及有色合金,2003(05):29-31+66-67.

Su Juanhua Li Hejun. Effects of Age hardening on the Properties of Cu Alloy Based on An Artificial Neural Network（ANN）[J]. Special Casting & Nonferrous Alloys, 2003,(5):29-31.
[1]苏娟华,李贺军,董企铭,刘平,康布熙.基于人工神经网络的铜合金时效性能研究[J].特种铸造及有色合金,2003(05):29-31+66-67. DOI：

Su Juanhua Li Hejun. Effects of Age hardening on the Properties of Cu Alloy Based on An Artificial Neural Network（ANN）[J]. Special Casting & Nonferrous Alloys, 2003,(5):29-31. DOI：

摘要

利用神经网络对Cu Cr Zr合金时效温度、时间与硬度和电导率样本集进行学习

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

可以对材料性能进行有效预测和分析。

Abstract

The BP neural network model（ANN） for age hardening process was established by introducing modified Levenberg-Marqurdt algorithm based on the learning of sample collection of the ANN to model the non-linear relation of hardness and electric conductivity of the Cu-Cr-Zr alloy as a function of ageing temperature and time. The results showed that the BP neural network could fully explore the domain knowledge in the sample collection to effectively predict and analyze the properties of the Cu-Cr-Zr alloy.