基于SPSS与BP网络的锌产量预测模型

SPSS and BP Artificial Neural Network and Its Application in Zinc Output forecast

金属锌对人们的生活具有非常重要的意义,为了使锌产量最大化,有必要对密闭鼓风炉铅锌熔炼操作参数进行优化,提出了基于SPSS与BP网络的密闭鼓风炉熔炼锌产量的预测模型;先对DCS系统上获得的在线数据建立初步的参变量与锌产量之间的因果关系,再利用SPSS统计分析软件中的主成分分析法对各参变量进行分析,最后将得到的与锌产量最相关的少数几个因素用BP网络建立预测模型,仿真结果表明,该方法减少了网络的训练时间,改善了学习效率,具有较高的预测精度,是可行的、有效的。

The metal zinc has very important significance to people＇s life , In order to maximize zinc output, It is necessary to optimize the parameters of the Imperial Smelting Furnace smelting zinc output , suggesting a zinc output forecasting model of Imperial Smelting Furnace based on SPSS and the BP network. At the first, set up a relationship between datas getting from DCS system and Zinc output, Second, make use of Principal Component Analysis in SPSS statistic analysis software to follow the analysis being in progress to every variable parameter and so get some variable parameters with Zinc output, At the last , set up a a zinc output forecasting model with that variable parameters based on BP network, that method has decreased by network training time , has improved the efficiency studying, has higher forecast accuracy , is feasible, effective.