KPCA and LS-SVM Prediction Model for Hydrogen Gas Concentration

Abstract

Hydrogen gas concentration forecasting and evaluation is very important for Bio-ethanol Steam Reforming hydrogen production. A lot of methods have been applied in the field of gas concentration forecasting including principal component analysis (PCA) and artificial neural network (ANN) etc. this paper used kernel principal component analysis (KPCA) as a preprocessor of Least Squares Support Vector Machine (LS-SVM) to extract the principal features of original data and employed the Particle Swarm Optimization (PSO) to optimize the free parameters of LS-SVM. Then LS-SVM is applied to proceed hydrogen gas concentration regression modeling. The experiment results show that KPCA-LSSVM features high learning speed, good approximation and generalization ability compared with SVM and PCA-SVM.