Nonpolynomial Spline Based Empirical Mode Decomposition

被引:0
|
作者
Singh, Pushpendra [1 ]
Srivastava, Pankaj Kumar [2 ]
Patney, Rakesh Kumar [1 ]
Joshi, Shiv Dutt [1 ]
Saha, Kaushik [3 ]
机构
[1] Indian Inst Technol, Dept Elect Engn, Delhi, India
[2] Jaypee Inst Informat Technol, Dept Math, Noida, India
[3] STMicroelect India Pvt Ltd, Noida, India
关键词
Empirical mode decomposition; mode mixing; non polynomial spline; intrinsic mode functions; detrend uncertainty; SOLVING DIFFERENTIAL-EQUATIONS; NUMERICAL-SOLUTION; COMPUTATIONAL TECHNIQUES; SYSTEM;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Authors propose a non polynomial spline based Empirical Mode Decomposition (EMD) algorithm to reduce mode mixing, and detrend uncertainty in analysis of time series. This new algorithm first locates original and pseudo extrema and then uses nonpolynomial spline interpolation to determine the upper and lower envelope at each decomposition step. A set of algebraic equations for the non polynomial spline interpolation is derived. A numerical simulation has been carried out for the analysis of error in spline interpolations. Various time series analysis have been preformed to show comparison among EMD and ensemble EMD (EEMD) based on polynomial spline, and non polynomial spline based EMD. Nonpolynomial spline based EMD algorithm is promising and generating better results.
引用
收藏
页码:435 / 440
页数:6
相关论文
共 50 条
  • [21] Information Hiding Based On Empirical Mode Decomposition
    Liang, LingFei
    Ping, ZiLiang
    PACIIA: 2008 PACIFIC-ASIA WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION, VOLS 1-3, PROCEEDINGS, 2008, : 536 - +
  • [22] Data Synthesis Based on Empirical Mode Decomposition
    Huang, Wen-Cheng
    Chu, Tai-Yi
    Jhang, Yi-Syuan
    Lee, Jyun-Long
    JOURNAL OF HYDROLOGIC ENGINEERING, 2020, 25 (07)
  • [23] Lasso Regression Based on Empirical Mode Decomposition
    Qin, Lei
    Ma, Shuangge
    Lin, Jung-Chen
    Shia, Ben-Chang
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2016, 45 (04) : 1281 - 1294
  • [24] Portfolio optimization based on empirical mode decomposition
    Yang, Li
    Zhao, Longfeng
    Wang, Chao
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 531
  • [25] Iris recognition based on empirical mode decomposition
    Han M.
    Peng Y.
    Zhang S.
    Sun W.
    Guangxue Xuebao/Acta Optica Sinica, 2010, 30 (02): : 364 - 368
  • [26] Iris Recognition Based on Empirical Mode Decomposition
    Han Min
    Peng Yuhua
    Sun Weifeng
    2008 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM AND KNOWLEDGE ENGINEERING, VOLS 1 AND 2, 2008, : 1054 - 1058
  • [27] GEARBOX FAULTDIAGNOSIS BASED ON EMPIRICAL MODE DECOMPOSITION
    Shen GuojiTao LiminChen ZhongshengCollege of Mechantronic Engineeringand Automation
    Chinese Journal of Mechanical Engineering, 2004, (03) : 454 - 456
  • [28] Signal denoising based on empirical mode decomposition
    Klionskiy, Dmitry
    Kupriyanov, Mikhail
    Kaplun, Dmitry
    JOURNAL OF VIBROENGINEERING, 2017, 19 (07) : 5560 - 5570
  • [29] An optimization based empirical mode decomposition scheme
    Huang, Boqiang
    Kunoth, Angela
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2013, 240 : 174 - 183
  • [30] ECG denoising based on the empirical mode decomposition
    Weng, Binwei
    Blanco-Velasco, Manuel
    Barner, Kenneth E.
    2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 1899 - +