Nonlinear Feature Selection Based on Hybrid KCCAFNN Algorithm for Modeling

摘要：

A hybrid algorithm based on kernel canonical correlation analysis (KCCA) and false nearest neighbor method (FNN) for selecting variables to reduce redundant feature and increate accuracy in nonlinear system modeling. In the proposed method, the KCCA can be employed to overcome difficulties encountered with the existing multicollinearity between the factors, the FNN can be used to calculate the variables’ map distance in the new KCCA feature space to select secondary variables. Comparing with the fully parametric model, the method is provided for the variable selection of nonlinear system modeling for the production processing of hydrogen cyanide.

关键词： variableselection nonlinearmodeling KCCA FNN kernelfunction

作者: YI Jun LI Taifu Su Yingying Hu Wenjin Gao Ting

作者单位: Department of Electrical and Information EngineeringChongqing University of Science and TechnologyCh Department of Electrical and Information Engineering Chongqing University of Science and Technology

会议类型: 国际会议

会议名称: 2011 International Conference on Business Management and Electronic Information(2011商业管理与电子信息国际学术会议 BMEI2011)

会议地点: 广州

会议语种:英文

页码: 1-4

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）