(中南工业大学 应用物理与热能工程系， 长沙 410083)
摘 要: 讨论了权值初始化、 变量的预处理、学习过程参数的自适应调节、 网络拓扑结构等因素对学习和推广的影响， 提出了一种改进的BP神经网络学习算法， 在很大程度上改善了学习效率。采用改进的带有8个输入变量的BP神经网络算法和自适应残差补偿算法建立吹炼终点组合预报模型。利用某厂实际生产数据进行仿真运行的结果表明， 利用该组合预报模型得到的平均相对预测误差为1.2%， 最大误差为4%。
关键字: 转炉； 铜锍吹炼； 神经网络；终点预报
（Department of Applied Physics and Heat Engineering， Central South University of Technology， Changsha 410083， P.R.China）
Abstract:The effect of mass initialization, variables pretreatment, adaptive adjustment of parameters and structure of network on exercvse and generalization was discussed in detail. An improved BP neural network exercise algorithm which was developed to greatly improve its efficiency was proposed, and a grouping prediction model based on the neural network algorithm with 8 input variables and error compensation of linear regression were developed. For simulating test, the average error of the model is 1.2%, and the maximal error of the model is 4%.
Key words: converting furnace； neural network； endpointprediction； linear regression
