Nonlinear Principal Component Analysis Based on Hierarchical Input-training Neural Network

Nonlinear principal component analysis(PCA) based on neural network with inputs training can effectively extract nonlinear principal components(PCs) from process variables,but the number of PCs can not be decided by training network,and the order of PCs can not be distinguished.In order to overcome these defaults,a hierarchical input-training neural network is proposed,and a nonlinear PCA based on this kind of network is presented,which can orderly find nonlinear PCs and quantitatively determine the number of PCs according to the prediction error of process data based on PCs.