计算机应用与软件2015，Vol.32Issue(5)：234-237,4.DOI:10.3969/j.issn.1000-386x.2015.05.057
应用 EMPCA算法对 TE 过程中随机及连续缺失数据进行补值的研究
IMPUTATION OF RANDOM AND CONTINUOUS MISSING VALUE IN TE PROCESS BY EMPCA
摘要
Abstract
Based on traditional principal component analysis, the EMPCA ( expectation maximisation principal component analysis) algorithm is applied to the imputation of random missing value and continuous missing value in Tennessee Eastman ( TE) process.The average relative error of the sample matrix and the sum of squared errors of load matrix are used as the indexes for evaluation.Results show that the use of EMPCA algorithm is able to effectively conduct imputation on random missing value and continuous missing value with significantly better effect than the mean value imputation.The error of the load matrix calculated from imputation is also relatively smaller.关键词
缺失数据补值/主元分析/EMPCA算法/TE过程Key words
Missing value imputation/Principal component analysis/EMPCA algorithm/TE process分类
信息技术与安全科学引用本文复制引用
孙怀宇,别圣平,李元..应用 EMPCA算法对 TE 过程中随机及连续缺失数据进行补值的研究[J].计算机应用与软件,2015,32(5):234-237,4.基金项目
国家自然科学基金项目( 60774070, 61034006,61174119). （ 60774070, 61034006,61174119）
