用BP神经网络预测热镀锌钢板的拉伸强度

Predictive Model of Properties on Hot Dip Galvanized Production by BP Neural Network

摘要: 运用BP神经网络,建立了热镀锌各工艺参数对热镀锌钢板力学性能影响的数学模型,并与线性回归模型进行了比较.结果表明：BP神经网络预测均方根偏差明显比线性回归预测均方根偏差小,表明该BP神经网络模型用于热镀锌板力学性能预测是可行的,并具有一定的实用性.

Abstract: A predictive model for mechanical properties of hot dip galvanized strip was established by BP neural network and compared with multi-variant linear regression model.The result shows that the root-mean-square deviation of BP neural network is less than that of multi-variant linear regression model,which proved that the predictive model for mechanical properties of hot dip galvanized strip is feasible and effective.