基于贝叶斯神经网络的带钢厚度预测与控制

采用贝叶斯统计学原理改进传统神经网络算法,通过在神经网络的目标函数中引入表示网络结构复杂性的约束项,避免网络的过拟合以提高网络的泛化能力.将改进的神经网络应用于济钢1700mm热连轧机带钢厚度预测中,其预报精度、训练时间和网络稳定性均优于传统神经网络预测;然后应用贝叶斯神经网络预测带钢塑性系数;最后将出口带钢厚度和带钢塑性系数的实时预测值综合应用于带钢热连轧厚度控制系统,改进了传统的厚度控制方式,进一步提高带钢质量.

Prediction and control of strip thickness based on Bayesian neural networks

The Bayesian statistical theory was adopted to improve traditional neural network algorithms, and constraints representing network structural complexity were introduced to the network objective function in order to avoid over-fitting the networks and enhance the generalization ability. The improved networks were applied to strip thickness prediction in Jigang 1700 mm mill, and the prediction result is superior to that of traditional neural networks in forecasting accuracy, training time and network stability. Then, the Bayesian neural networks were used to predict the plasticity coefficient of strips. Finally, the real-time forecasts of exit thickness and plasticity coefficient of strips were synthetically utilized in the thickness control system of hot strip rolling to improve strip quality further.