东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (4): 468-471.DOI: -

• 论著 • 上一篇    下一篇

基于高斯过程的改进经验模态分解及应用

闻时光;王斐;吴成东;张育中;   

  1. 东北大学信息科学与工程学院;哈尔滨工业大学机器人技术与系统国家重点实验室;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60705031);;

Using a Gaussian process to improve and utilize empirical mode decomposition

Wen, Shi-Guang (1); Wang, Fei (1); Wu, Cheng-Dong (1); Zhang, Yu-Zhong (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) State Key Laboratory of Robotics and Systems (HIT), Harbin 150001, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Wen, S.-G.
  • About author:-
  • Supported by:
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摘要： 传统Hilbert-Huang变换(HHT)的经验模态分解算法是基于3次样条插值的包络线计算方法,存在过冲及边界效应等缺点.针对传统经验模态分解算法求解包络线存在的问题,提出了基于高斯过程回归的改进包络线插值方法.并且讨论了如何优化高斯过程参数,提高了泛化能力及包络线的插值精度,较好地改进了HHT的虚假频率和端点效应问题.通过处理步态数据的试验表明,采用高斯过程方法可以较好地改进HHT存在的虚假频率和端点效应问题,减小了固有模态函数的失真.

关键词: 经验模态分解, 高斯过程, 插值, 步态识别

Abstract: The traditional Hilbert-Huang transform (HHT) based on the envelope line calculation of the cubic spline interpolation has several drawbacks such as overshooting and endpoint oscillation. To solve this problem, a new algorithm based on Gaussian process regression is proposed to compute the envelope. Optimizing the parameters of Gaussian process regression is discussed, with improved generalization ability and interpolation precision. The algorithm effectively resolves the false frequency and endpoint oscillation problems. Gait data processing indicates that the problem of spurious frequency and endpoint oscillation can be preferably improved with a Gaussian process, and distortion of mode function was decreased.

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