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
2006年
5期
597-603
,共7页
chemical process modelling%input training neural network%nonlinear principal component analysis%naphtha pyrolysis
Many applications of principal component analysis (PCA) can be found in dimensionality reduction.But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on improved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Momentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments.Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propylene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
