针对软传感器建模过程中，高维冗余的非线性辅助变量造成的维度灾难问题，提出一种结合核独立成分分析法(Kernel independent component analysis, KICA)与虚假最近邻点法(False nearest neighbors, FNN)的非线性辅助变量选择方法。主要利用核函数将原始非线性数据映射到线性子空间，并采用独立成分分析消除因子之间的多重共线性，再运用虚假最近邻点法，计算原始数据在KICA子空间中投影的距离，依次判断各辅助变量对主导变量的解释能力，由此进行非线性变量选择。以某企业氢氰酸(Hydrocyanic acid, HCN)生产工艺过程中的转化率为软传感器预测目标，仿真结果表明该方法可有效降低辅助变量的维数、同时提高模型的预测精度。

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear soft sensor. A novel method based on kernel independent component analysis (KICA) and false nearest neighbors method (FNN) is proposed on selecting the most suitable secondary process variables. The first step is to convert the non-linear operating variables into the linear space with kernel method. One the basis, they are projected into the independent ones with KICA transformations. In order to compare the different impacts on the operating variables, each original variable is eliminated orderly from original datasets with FNN in KICA subspace. In this way, it is possible to trace the important cause for the prediction. The result shows its validity with the verification in hydrocyanic acid (HCN) process industry.