Research on the Prediction Model for Recovery Rate of Alloying Elements

摘要：

　　It is well known that alloying model has great effect on steel quality,and the precision of alloying model relies heavily on the calculation of recovery rate of alloying elements.Aiming at steel quality improvement,this paper firstly built a recovery rate prediction model with BP neural network,and then worked on this model with the help of LM and POS algorithms respectively.The comparison of simulation shows that,the PSO algorithm can overcome the shortcomings of local minimum and improve the precision of convergence to a certain extent.The simulation results confirmed the high efficiency of this algorithm.

关键词： Recovery Rate of Alloying Elements PSO LM Algorithm Neural Network and Prediction Model

作者: Xiaoke Fang Jianhui Wang Wenle Zhang

作者单位: College of Information Science and Engineering, Northeastern University, Shenyang 110004

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 2204-2208

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）