A Variable Selection Method Based on KPCA and FNN for Nonlinear System Modeling
The kernel principal components analysis (KPCA) can be used to convert a set of nonlinear variables into a linearly separable factors and overcome difficulties encountered with the existing multicollinearity between the factors. However the nonlinear system modeling method does not reduce the number of original features. This paper presents a novel method based on KPCA and selection of false nearest neighbor method (FNN) for secondary variables selection. In the proposed approach, it is inspired by FNN that interpretation of primary variable would be estimated by calculating the variables’ map distance in the KPCA space to select secondary variables. The results show that the method is effective and suitable for variable selection by comparing with the fully parametric model form the production processing of hydrogen cyanide.
Variableselection Nonlinearsystems Modeling KPCA FNN
YI Jun LI Taifu Su Yingying Hu Wenjin Gao Ting
Department of Electrical and Information Engineering Chongqing University of Science and Technology Department of Electrical and Information EngineeringChongqing University of Science and TechnologyCh
国际会议
广州
英文
1-4
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）