中文摘要：

特征变量选择技术是非线性系统建模过程中降低信息冗余和提高精度的有效方法。提出一种结合核主成分分析法（Kernel principal components analysis,KPCA）与虚假最近邻点法（False nearest neighbor,FNN）的变量选择法。引入核方法,将非线性原始数据映射到线性空间,再采用主成分分析法有效合理地消除因子之间的多重共线性,受混沌相空间虚假最近邻点法的启示,通过计算原始数据在KPCA子空间中投影的距离,判断其对主导变量的解释能力,由此进行变量的选择该方法用氢氰酸生产工艺工程中的非线性模型验证,并与全参数模型进行比较,结果显示该方法有良好的变量选择能力。因此,该研究为非线性系统建模的变量选择方法提供一种新方法。

英文摘要：

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling.A novel method based on kernel principal components analysis（KPCA） and false nearest neighbor method（FNN） is proposed on select the most suitable secondary process variables used as nonlinear modeling inputs.In the proposed approach,the KPCA can be employed to overcome difficulties encountered with the existing multicollinearity between the factors.In the new KPCA feature subspace,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.Nonlinear model form the production processing of hydrogen cyanide is used to verify the validity of the method,and compared with the fully parametric model.The results show that the method is effective and suitable for variable selection.Therefore,a new method is provided for the variable selection of nonlinear system modeling.