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机构地区:[1]解放军理工大学野战工程学院,南京210007
出 处:《机械强度》2015年第6期1017-1022,共6页Journal of Mechanical Strength
基 金:国家自然科学基金项目(51175511)资助~~
摘 要:针对总体平均经验模式分解(EEMD)中存在的端点效应问题,考虑到极值延拓只利用端点附近的值、基于数据点的支持向量回归机(SVR)延拓耗时长的缺点,将极值延拓和SVR数据预测延拓结合起来,提出了一种基于极值点的SVR延拓新方法。该方法通过找出信号中的所有极值点,先将端点附近的极值点作为SVR训练样本对信号极值点幅值进行延拓,并利用原信号所有相邻极值点时间尺度之差的平均值来控制延拓极值点的形状,再利用埃尔米特插值将延拓的极值点插值成所需要的数据点完成对信号的延拓。方法既参照了信号两端的变化趋势,又综合考虑了整个数据序列内部极值点的信息。通过仿真信号和对端点效应影响严重的实测液压故障信号分析表明,基于极值点的SVR延拓方法不仅提高了EEMD分解的精度和可靠性,有效地抑制了端点效应现象,解决了端点效应引起的分解失真问题,而且大幅减少了SVR延拓需要延拓的数据点,明显缩短SVR延拓的时间,提高了方法的实用性。For the end effect of ensemble empirical mode decomposition( EEMD),considering the disadvantages that the extreme extension only uses the value near the endpoints and the support vector regression( SVR) extension based on data points takes much time,a new SVR extension method based on extreme points is proposed by the combination of extreme extension and SVR extension. Firstly,extreme points near the endpoints are used as the SVR training samples to extend extreme value.Secondly,the average difference between the original signal time scales of the adjacent extreme points is used to control the shape of these extended extreme points. Finally,these points are interpolated to the required data points by Hermite. The method refers to the variation of signal endpoints,meanwhile,considers the information inside extreme points of the entire data sequence.Simulation and experimental short signal results show that SVR extension method based on extreme points not only can improve the accuracy and reliability of the EEMD decomposition,depress end effect effectively and solve the decomposition distortion caused by end effect,but also sharply reduce the number of SVR extension data points,distinctly shorten the time of SVR extension and increase the practicability of the method.
关 键 词:信号延拓 总体平均经验模式分解 端点效应 支持向量回归机 埃尔米特插值
分 类 号:TN911.7[电子电信—通信与信息系统] TH165.3[电子电信—信息与通信工程]
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