动态称量经验模态分解数据处理方法  

Data processing method of empirical mode decomposition on dynamic weighting

作　　者：张西良[1] 万学功[1] 李萍萍[1] 张建[1] 徐云峰[1] 

机构地区：[1]江苏大学机械工程学院,江苏镇江212013

出　　处：《江苏大学学报（自然科学版）》2008年第6期461-464,共4页Journal of Jiangsu University：Natural Science Edition

基　　金：江苏省教育厅自然科学基金资助项目(03KJD410071)

摘　　要：为消除动态称量信号中的各种噪声,研究动态称量经验模态分解数据处理方法.针对经验模态分解筛分过程中数据序列的两端常处于非极值状态,而导致边缘效应,以及现有抑制边缘效应方法效率低、对数据量要求大的不足,提出一种新的数据延拓方法次端点镜像延拓法来抑制边缘效应.通过对定量加料动态称量经验模态分解试验,结果表明新的抑制边缘效应方法可以获得较高的称量精度和效率,对称量信号分解的最大误差在±0.8%以内.To eliminate noises involved in dynamic weighting signal, empirical mode decomposition （EMD） is applied. For the end points of data sequence being usually not the extremes, the upper and lower envelopes swing sharply there, which cause end effect and result in low precision of the sifting results. A new method called the second extremes mirror method is proposed, considering the given antiend-effect method with low precision and strict requirement for data volume. The improved EMD method is adopted in dynamic weighting trial. The results indicate that the precision of this new method is very high, the new anti-end-effect method is the most effective one among the given methods, and the error of weighting can be controlled below 0.8%.

关 键 词：称量 数据处理 经验模态分解 边缘效应 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] TB487[自动化与计算机技术—控制科学与工程]