摘要
针对PCA(PrincipalComponentsAnalysis)技术中,由于重叠信息会严重影响主成分的正确提取这一问题,提出了一种改进的数据降维处理方法·首先,利用标准化变量间的相关系数大小找到重叠信息·然后,将重叠信息进行加权综合·最后,利用改进的数据降维处理方法以及分布式网络技术,建立了基于PCA DRBFN(PrincipalComponentsAnalysis DistributedRadialBasisFunctionNetwork)的软测量模型,并将其应用到某钢厂的精苯精馏过程,对精苯干点进行估计·通过仿真证明,所建立的模型具有较好的泛化效果·
An improved data dimension decreasing method is proposed to reduce the overlapped information in multivariable system,since a number of overlapped information will affect greatly the correct pick-up of PCs(Principal Components) in PCA(Principal Components Analysis). The overlapped information can be found using the correlation coefficients between standardized variables, then they are weighted and integrated altogether. An PCA-DRBFN(Principal Components Analysis-Distributed Radial Basis Function Network) based soft-sensing model is thus developed using the improved method. The model has been applied to the pure benzene rectifying process in a steel plant to estimate the dry point of pure benzene. Simulation results showed that the proposed method and developed model are both favorably versatile.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第2期103-105,共3页
Journal of Northeastern University(Natural Science)
基金
辽宁省自然科学基金资助项目(002013)
关键词
软测量
主成分分析(PCA)
数据降维
重叠信息
精苯精馏
径向基网络
soft-sensing
principal component analysis(PCA)
data dimension decreasing
information overlap
pure benzene rectification
radial basis function network
