BP神经网络漏钢预测系统优化

Optimization for breakout prediction system of BP neural network

针对传统逻辑漏钢预测系统稳定性差、收敛速度慢、收敛精度低等缺点,建立具有自组织、自学习等功能的误差反向传播BP神经网络预测模型.采用变步长并加入动量项、防振荡项等方法,使网络训练过程能够跳出局部极小,加快了收敛速度.系统改变以往只将温度数据作为输入参数的传统,将拉速、中间包钢水温度作为考虑因素,扩大了漏钢因素的考虑范围.实验结果表明,采用BP神经网络对某炼钢厂实际数据进行漏钢预测,预报结果准确,具有较好的在线应用前景.

In order to overcome the problems of slow speed and low accuracy of convergence,and the shortcomings of poor stability of the traditional logical prediction of breakout system,this paper designs a breakout predicting model based on BP neural network which is capable of self-organize and self-learn. The BP algorithm is modified to improve its learning speed such as changing study rate,adding momentum item and avoiding vibration item,so the network can escape from the local minimum while it is training. The drawing speed and temperature of molten steel in tundish are regarded as the influencing factors of breakout in model to extend the range of breakout factors. The experimental results show that the system predictes to get exact results based on practical data from the field in a steel plant,so it has good anticipant practical application on line in predicting breakout.