Research on Grey Incidence Measurement Method Based on Dynamic Time Warping Distance

被引:0
|
作者
Dai, Jin [1 ]
Hu, Feng [1 ]
Liu, Xin [1 ]
机构
[1] Chongqing Univ Posts & Telecommun, Coll Software Engn, Chongqing 400065, Peoples R China
来源
JOURNAL OF GREY SYSTEM | 2015年 / 27卷 / 01期
基金
中国国家自然科学基金;
关键词
Grey Theory; Grey Incidence Analysis; Degree of Grey Incidence; Dynamic Time Warping;
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Grey incidence measurement (GIM) method is core content of grey analysis. As the traditional GIM methods only handle sequences with the length, the sequences with different lengths must be padded by deleting data, mean statistic method or GM(1,1) model prediction. It results in the degree of grey being enlarged and loss useful information. On the basis of dynamic time warping (DTW) distance, a novel degree of grey incidence (DTW-Degree) is proposed. It measures the degree of grey incidence by the shortest path in the distance matrix of sequences and does not need to pad any data of sequences. Meanwhile, the corresponding GIM to sequences with different lengths based on DTW (DTW-GIM) is constructed. If is an effective solution to solve the problems, such as the incidence measurement between grey sequences with different length, timeline stretching and bending. Simulation results show that the DTW-GIM is correct and effective. When the GIM Methods based on Deng-Degree and generalized degree are failed, DTW-GIM still achieves a reliable quantitative analysis conclusion.
引用
收藏
页码:117 / 126
页数:10
相关论文
共 50 条
  • [31] Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping
    Shang Zhiwu
    Yu Yan
    Geng Rui
    Gao Maosheng
    Li Wanxiang
    INSIGHT, 2021, 63 (08) : 465 - 471
  • [32] Asynchronous Track-to-Track Association Algorithm Based on Dynamic Time Warping Distance
    Yang Yanting
    Liang Yan
    Yang Yanbo
    Qin Yuemei
    2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 4772 - 4777
  • [33] FEATURE BASED DYNAMIC TIME WARPING
    Ying Xie
    Li Fangping
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER THEORY AND ENGINEERING (ICACTE 2009), VOLS 1 AND 2, 2009, : 1785 - 1792
  • [34] Fault Recognition of Indicator Diagrams Based on the Dynamic Time Warping Distance of Differential Curves
    Du, Yi
    Zhao, Peng
    Zhang, Ting
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2021, 2021 (2021)
  • [35] Dynamic Time Warping Distance for Message Propagation Classification in Twitter
    Jendoubi, Siwar
    Martin, Arnaud
    Lietard, Ludovic
    Ben Yaghlane, Boutheina
    Ben Hadji, Hend
    SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2015, 2015, 9161 : 419 - 428
  • [36] AN IMPROVED DYNAMIC TIME WARPING METHOD COMBINING DISTANCE DENSITY CLUSTERING FOR EYE MOVEMENT ANALYSIS
    Wang, Xiaowei
    Li, Xubo
    Wang, Haiying
    Zhao, Wenning
    Liu, Xia
    JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2023, 23 (02)
  • [37] Dynamic Time Warping and the (Windowed) Dog-Keeper Distance
    Bachmann, Joerg P.
    Freytag, Johann-Christoph
    SIMILARITY SEARCH AND APPLICATIONS, SISAP 2017, 2017, 10609 : 127 - 140
  • [38] Monitoring method of vehicle axle temperature based on dynamic time warping
    Cao, Yuan
    Wang, Yu-Jue
    Ma, Lian-Chuan
    Chen, Lei
    Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering, 2015, 15 (03): : 78 - 84
  • [39] Action recognition based on Fast Dynamic-Time Warping Method
    Vajda, Tamas
    2009 IEEE 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, PROCEEDINGS, 2009, : 127 - 131
  • [40] Movement Classification based on Acceleration Spectrogram with Dynamic Time Warping Method
    Noh, Byeongjoon
    Cha, KeumGang
    Chang, Seongju
    2017 18TH IEEE INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (IEEE MDM 2017), 2017, : 397 - 400