关键词: 过程控制, 主主成分分析, 径向基神经网络, 软测量, 在线预测

Abstract: Aiming at the on-line prediction and monitoring of key process variables in process control system,by combining Principal component Analysis (PCA) and Radial Basis Function Neural Network (RBFNN),an improved PCA-RBFNN soft measurement modeling method was proposed.The PCA analysis variable selecting method was used to find the process variable subset with the best interpretation ability for the system process characteristics.Then,the process variable subset was taken as the input and the estimated variable as the output to establish the improved PCA-RBFNN model.K-means clustering method and the least mean square error method were used to initialize the data center,expansion coefficient and connection weight of RBFNN.The gradient descent method was used to train and correct the model.Taking the production process of a textile raw material reactor as an example,the improved PCA-RBFNN model was verified and the output performance was compared and analyzed.The results showed that the improved PCA-RBFNN model could realize online prediction of process variables,improve the stability and reliability of the process control system,which had better generalization ability,prediction ability and output accuracy.

Key words: process control, principal component analysis, radical basis function neural network, soft measurement, online prediction