基于AP聚类的RBF神经网络研究及其在转炉炼钢中的应用  

RBF Neural Network Base on Affinity Propagation Clustering and Its Application in BOF Steelmaking

作　　者：张辉宜[1] 周奇龙[1] 袁志祥[1] 刘志明 

机构地区：[1]安徽工业大学计算机学院,安徽马鞍山243002 [2]韶钢第三炼钢厂,广东韶关512123

出　　处：《钢铁研究学报》2014年第1期22-26,共5页Journal of Iron and Steel Research

基　　金：安徽省教育厅自然科学重点研究项目(KJ2009A136)

摘　　要：针对RBF神经网络学习算法不能很好地确定其隐含层节点数、隐含层节点中心及其半径的问题,利用AP聚类算法无需事先确定聚类数的特点,提出了一种基于AP聚类的RBF神经网络算法。将该算法应用于120t转炉Q235B钢种冶炼过程的终点碳含量和温度预报,预测结果与实际结果比较,表明该算法具有预测精度高等优点,可为类似应用提供借鉴。The number, center points and width of hidden layer units of RBF Neural Network are hard to be deter- mined. AP （Affinity Propagation） Clustering doesntt need to known the number of cluster first. Using this advan- tage of AP Clustering, an RBF neural network algorithm base on affinity propagation clustering was proposed to solve the problems above. The end-point temperature and carbon content of steel grade of Q235B of a 120t con- verter of a steel factory were predicted using the algorithm and the prediction value and actual value was compared. The result shows that this algorithm has a high precision on the condition of higher requirements of the target pa- rameter errors. And it can provide a reference for similar aoolications.

关 键 词：AP聚类 RBF神经网络 转炉炼钢 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]