基于特征子空间虚假邻点判别的软传感器模型变量选择

Reduction of Secondary Variables on Soft Sensor Model Based on False Nearest Neighbours in Feature Subspace

辅助变量选择技术是软传感器建模过程中降低信息冗余和提高效率的有效方法。提出一种结合偏最小二乘回归法与虚假最近邻法的变量选择法。采用偏最小二乘回归法有效合理地消除因子之间的多重共线性,在一个新的正交空间里,受混沌相空间虚假最近邻点法的启示,通过计算某变量选择前后在特征子空间里的相关性,判断其对主导变量的解释能力,由此进行变量的选择,利用偏最小二乘法得到软测量模型。该方法通过构造的试验和Jolliff变量选择试验作了验证,结果显示该方法有良好的辅助变量选择能力,为软传感器建模的辅助变量选择方法提供了一种新方法。

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in soft sensor modeling.A novel method based on partial least-squares（PLS） regression method and false nearest neighbor（FNN） method is proposed for selecting the most suitable secondary process variables used as soft sensing inputs.In the proposed approach,the PLS regression method is employed to overcome difficulties encountered with the existing multicollinearity between the factors.In a new orthogonal space,inspired by chaos phase space FNN method,through calculation of the relativities of a certain variable in the feature subspace before and after selection,its interpretation of primary variable can be estimated,then selection of variables is carried out,and the least square method is used to obtain a soft-sensing model.This method is verified through structure test and Jolliff variable selection test,and the results demonstrate that it has good capability of secondary variable selection.