摘要
Methods of PCA (principal component analysis) and PLS (partial least squares) based on RBF (radial basis function)neural network are proposed for the reason that the generalization ability of common neural networks debases when the input data is high dimension or correlations exist These two methods can reduce the dimension and extract the correlations of the input data They are used in the prediction of polypropylene melt index, and the simulation results show that the statistical methods improve the predictive precision
Methods of PCA (principal component analysis) and PLS (partial least squares) based on RBF (radial basis function)neural network are proposed for the reason that the generalization ability of common neural networks debases when the input data is high dimension or correlations exist These two methods can reduce the dimension and extract the correlations of the input data They are used in the prediction of polypropylene melt index, and the simulation results show that the statistical methods improve the predictive precision successfully
出处
《化工学报》
EI
CAS
CSCD
北大核心
2003年第8期1160-1163,共4页
CIESC Journal
关键词
径向基神经网络
主元分析法
偏最小二乘法
熔融指数
RBF neural network,PCA (principal component analysis),PLS (partial least squares),MI (melt index)
