中国化学工程学报(英文版)
中國化學工程學報(英文版)
중국화학공정학보(영문판)
CHINESE JOURNAL OF CHEMICAL ENGINEERING
2008年
5期
746-751
,共6页
傅永峰%苏宏业%张英%褚健
傅永峰%囌宏業%張英%褚健
부영봉%소굉업%장영%저건
soft sensor%fuzzy c-means%incremental support vector machines%heuristic sample displacement method%P-xylene purlty2%accepted 2008-05-28
To overcome the problem that soft sensor models cannot be updated with the process changes.a soft sensor modeling algorithm based on hybrid fuzzy C-means(FCM、algorithm and incremental support vector ma-chines(ISVM)iS proposed.This hybrid algorithm FCMISVM includes three parts:samples clustering based on FCM algorithm.leaming algorithm based on ISVM.and heuristic sample displacement method.In the training process,the training samples are first clustered bv the FCM algorithm.and then by training each clustering with the sVM algorithm.a sub-model is built to each clustering.In the predicting process.when an incremental sample that represents new operation information is introduced in the model,the fuzzy membership function of the sample to each clustering is first computed bv the FCM algorithm.Then.a corresponding SVM sub.model of the clustering with the largest fuzzy membership function iS used to predict and perform incremental learning SO the model can be updated on-line.An old sample chosen by heuristic sample displacement method iS then discarded from the sub-model to control the size of the working set.The proposed method is applied to predict the P-xylene(PX)purity in the adsorption separation process.Simulation results indicate that the proposed method actually increases the model'S adaptive abilities to various operation conditions and improves its generalization capability.