摘要
建立了粗汽油干点软测量模型。模型分别采用多元逐步回归方法和反向传播方法。结果表明:多元逐步回归方法可筛选自变量,但会将一些重要因素剔除;而神经网络可通过预选输入单元确定网络结构。通过对建立好的模型进行预测,可获得较满意的粗汽油干点值。
This paper set up soft-sensor model of crude gasoline endpoint. The model used multiple stepwise regression method and back propagation method independently. The result showed that the multiple stepwise regression method could make a selection from many free variables, but it could get rid of some important factors; and the neural network could decide the structure of network by pre-input units. We got a good estimation of crude gasoline endpoint by the model.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2003年第6期845-847,共3页
Computers and Applied Chemistry
基金
广东省自然科学基金(960265)
关键词
多元逐步回归
反向传播方法
粗汽油干点
软测量
模型
产品质量
soft-sensing
crude gasoline endpoint
back propagation method
multiple stepwise regression method