南京理工大学学报（自然科学版）2017，Vol.41Issue(5)：574-580,7.DOI:10.14177/j.cnki.32-1397n.2017.41.05.006
一种基于改进扩张搜索聚类算法的软测量建模方法
Soft sensor modeling method based on improved expanding searching clustering algorithm
摘要
Abstract
An improved expanding searching clustering algorithm is proposed to overcome the shortcomings of the traditional clustering methods relying on data space distribution and prior knowledge too much. In consideration of the effects of the sample density on the searching radlus,the improved algorithm selects different searching radius according to the density of each sample point. For all sample distribution shapes, the threshold value is applied to choose different clustering methods relying on different density points. Sample data is clustered by using the improved expanding searching clustering algorithm. All soft sensor models are built up by Gaussian process regression ( GPR) . The final model is formed by using the switch fusion mode according to the results of clustering. A sample of a bisphenol-A production crystallization unit is applied to make a simulation for building the soft-sensor model of the phenol concentration at the exit device and the good experiment results are obtained.关键词
疏密度/阈值/高斯过程回归/扩张搜索聚类算法/软测量建模Key words
densities/threshold/Gaussian process regression/expanding searching clusting algorithm/soft sensor modeling分类
信息技术与安全科学引用本文复制引用
张孙力,杨慧中..一种基于改进扩张搜索聚类算法的软测量建模方法[J].南京理工大学学报（自然科学版）,2017,41(5):574-580,7.基金项目
国家自然科学基金(61273070) （61273070）
江苏省高校优势学科建设工程资助项目 （）
