1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China 2.Ningbo Iron and Steel Co., Ltd., Ningbo 315000, Zhejiang, China
Abstract：On the basis of optimizing the traditional BP neural network model, a neural network prediction model combined with grey theory was developed by laying correlative degree weights to all input factors which had effects on the output variable. And then simulation experiments of model newly established were conducted based on data from a domestic steel plant. The results show that hit rate arrives at 65.00% when the error modulus is less than 500%, and the value is 96.67% when less than 10.00%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 12.50%, Thus, the prediction of end point phosphorus content fits the real perfectly, which accounts for that neural network model for terminative phosphorus content based on grey theory can reflect accurately the practice in hot metal pretreatment.
收稿日期: 2011-05-23
出版日期: 2012-03-20
引用本文:
张慧宁 ，徐安军，崔健 ，贺东风，田乃媛. 基于灰色理论的铁水预处理终点磷含量神经网络预测模型[J]. 钢铁, 2012, 47(3): 38-41.
ZHANG Hui-ning1，XU An-jun1，CUI Jian2，HE Dong-feng1，TIAN Nai-yuan1. Neural Network Prediction Model for End Point Phosphorus Content Based on Grey Theory in Hot Metal Pretreatment. Iron and Steel, 2012, 47(3): 38-41.
2012, Vol. 47 
