烧结矿碱度预测中的多传感器信息融合技术  

Prediction of R in Sintering Process Based on Muitisensor Information Fusion

作　　者：宋强[1] 鲍雅萍[1] 李华[2] 

机构地区：[1]安阳工学院机械工程系,河南安阳455000 [2]安阳钢铁集团公司,河南安阳455004

出　　处：《计算机测量与控制》2008年第12期1876-1878,共3页Computer Measurement &Control

摘　　要：烧结矿碱度的测量是钢铁工业中的关键和难点,况且又容易受到烧结几乎每一个操作环节的影响;据此提出利用BP神经网络进行多传感器数据融合的烧结矿碱度的预报模型;通过对现场实际数据进行仿真,表明该方法鲁棒性强,准确性高,泛化能力广,具有很强的实用性和推广价值。The measurement of R in sintering process is difficult to control, on the other hand, it is easily to be disturbed by almost every process step. A prediction model of R in sintering process based on BP neural network is proposed to judge the trend of R. The application result shows that the prediction with this method can achieve higher robust, better utility and extension value.

关 键 词：多传感器数据融合 神经网络算法 碱度 权值和阈值 样本数据 

分 类 号：TP306[自动化与计算机技术—计算机系统结构]