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一种基于LDA和FCM的BPA多模型软测量方法
引用本文:陈金凤,杨慧中,邓玉俊.一种基于LDA和FCM的BPA多模型软测量方法[J].华东理工大学学报(自然科学版),2010,36(1).
作者姓名:陈金凤  杨慧中  邓玉俊
作者单位:江南大学通信与控制工程学院,江苏,无锡,214122
基金项目:国家自然科学基金,江苏省高技术研究(工业),江南大学创新团队发展计划 
摘    要: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。

关 键 词:模糊聚类  线性判别分析  软测量

A Multi-model Soft Sensor Method of BPA Based on LDA and FCM
CHEN Jin-feng,YANG Hui-zhong,DENG Yu-jun.A Multi-model Soft Sensor Method of BPA Based on LDA and FCM[J].Journal of East China University of Science and Technology,2010,36(1).
Authors:CHEN Jin-feng  YANG Hui-zhong  DENG Yu-jun
Abstract: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.
Keywords:fuzzy clustering  linear discriminant analysis  soft sensor
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