摘要
提出一种新的软测量方法 ,通过建立过程变量非线性主元得分与产品质量参数之间的三层前向神经网络模型 ,得到产品质量参数的预测值。实际应用表明 ,该方法比常规的线性主元分析方法和神经网络方法具有更好的预测性能。
A new soft sensor based on nonlinear principal component analysis (PCA) is developed. A three-layer feedforward neural network model is established to approximate the relationship between the scores of the nonlinear principal components and the estimated variable. The proposed method is applied on an industrial crude oil atmospheric distillation tower and illustrated by comparisons with other familiar methods. The results show that the proposed method gives a better performance over the conventional PCA method and neural network method.
出处
《化工自动化及仪表》
EI
CAS
北大核心
2004年第4期19-20,34,共3页
Control and Instruments in Chemical Industry
关键词
非线性主元分析
常压塔
软测量
神经网络
nonlinear principal component analysis
atmospheric tower
soft sensor
neural network
