一种基于LDA和FCM的BPA多模型软测量方法

A Multi-model Soft Sensor Method of BPA Based on LDA and FCM

模糊C-均值聚类(FCM)算法是数据预处理中常用的一种方法,但用这种方法进行数据聚类,各类别边界信息间往往存在干扰,模型精度不能得到很好改善。本文采用一种改进的线性判别分析(LDA)方法,用于扩大样本类别间的距离,使聚类更为精确。将FCM算法与改进的LDA算法结合提取样本特征,然后通过多模型融入到SVM算法中。通过对双酚A软测量建模的仿真研究表明该方法具有较好的效果。

Fuzzy C-means clustering （FCM） algorithm is a method that often used in data pretreatment, but it can bring the problem on interference between border information at the same time, so the accuracy of model can＇t be improved a lot. Linear discriminant analysis （LDA） is an effective method used to expand the limits of the samples which can make clustering more precise. The combination of FCM and improved LDA is applied to SVM by multi-models method, which can reinforce the algorithm. The application in the soft sensor modeling of BPA shows that this method has a certain practicality.