计算机应用研究2016，Vol.33Issue(12)：3713-3716,4.DOI:10.3969/j.issn.1001-3695.2016.12.043
一种基于特征子空间的改进动态核主元分析方法
Improved method of dynamic kernel principal component analysis based on feature subspace
摘要
Abstract
For large sample data sets,traditional DKPCA occupancy a lot of computer memory and large computation,in order to solve these problems,this paper proposed an improved DKPCA based on effective feature subspace(EFS-DKPCA).The new method based on a orthonormal basis of the sub-space spanned by the training samples mapped onto the smaller feature space to simplify K,thereby reducing DKPCA computational complexity.When applied to process monitoring,the EFS-DKPCA-based method was more efficient in computation and needed less computer memory than DKPCA-based methods.Computer simulation of TE process demonstrates the effectiveness and efficiency of the proposed method.关键词
动态核主成分分析/特征空间/特征提取/故障检测/TE过程Key words
dynamic kernel principal component analysis(DKPCA)/feature space/feature extractor/fault detection/ten-nessee eastman process分类
信息技术与安全科学引用本文复制引用
刘春燕,于春梅,闫广峰..一种基于特征子空间的改进动态核主元分析方法[J].计算机应用研究,2016,33(12):3713-3716,4.基金项目
特殊环境机器人技术四川省重点实验室开放基金资助项目 （）
