基于SVR的经验模态分解端点延拓改进方法  

Improved method for endpoint extension of empirical mode decomposition based on SVR

作　　者：王新[1] 王乾[1] 赵志科[2] 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000 [2]中国矿业大学机电工程学院,江苏徐州221116

出　　处：《计算机工程与应用》2014年第9期204-208,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61340015);河南省高校科技创新人才支持计划项目(No.2008HASTIT022)

摘　　要：针对经验模态分解过程中产生的端点效应问题,提出了将镜像延拓和支持向量回归机相结合的端点延拓改进方法。利用支持向量回归机对原始信号的极值点数据序列两端进行预测,用镜像延拓法确定所预测极值点的位置。该改进方法解决了支持向量回归机对长数据序列预测不准确,以及镜像延拓法对端点不是极值点的短数据序列处理效果不佳等问题。引入六个评价标准,对端点延拓方法的效果进行了分析。结果表明,该改进方法能有效地抑制经验模态分解产生的端点效应。In order to solve the endpoint effect in the empirical mode decomposition, an improved method combining the mirror extension with the Support Vector Regression（SVR）is proposed. In the improved method, the SVR method is applied to predicting extreme points on both ends of the extreme points of the original signal, and then the mirror extension method is applied to determining the position of the predicted extreme points. The improved method can solve the inaccu-rate prediction on the long data sequence by using the SVR method separately, and the problem that the boundary of the short time sequence is not the extreme point by using the mirror extension method separately. At the end, the endpoint extension effect is analyzed by using the evaluation criteria. The simulation results show that the improved method can effec-tively restrain the endpoint effect of the empirical mode decomposition.

关 键 词：经验模态分解 端点效应 镜像延拓 支持向量回归机 

分 类 号：TH165.3[机械工程—机械制造及自动化]