<listing id="l9bhj"><var id="l9bhj"></var></listing>
<var id="l9bhj"><strike id="l9bhj"></strike></var>
<menuitem id="l9bhj"></menuitem>
<cite id="l9bhj"><strike id="l9bhj"></strike></cite>
<cite id="l9bhj"><strike id="l9bhj"></strike></cite>
<var id="l9bhj"></var><cite id="l9bhj"><video id="l9bhj"></video></cite>
<menuitem id="l9bhj"></menuitem>
<cite id="l9bhj"><strike id="l9bhj"><listing id="l9bhj"></listing></strike></cite><cite id="l9bhj"><span id="l9bhj"><menuitem id="l9bhj"></menuitem></span></cite>
<var id="l9bhj"></var>
<var id="l9bhj"></var>
<var id="l9bhj"></var>
<var id="l9bhj"><strike id="l9bhj"></strike></var>
<ins id="l9bhj"><span id="l9bhj"></span></ins>
Volume 20 Issue 5
Aug.  2021
Turn off MathJax
Article Contents
Liao Ming, Zhang Wenming, Fang Mei, Feng Yali. Discriminating Fractals In Time Series[J]. Chinese Journal of Engineering, 1998, 20(5): 412-416. doi: 10.13374/j.issn1001-053x.1998.05.002
Citation: Liao Ming, Zhang Wenming, Fang Mei, Feng Yali. Discriminating Fractals In Time Series[J]. Chinese Journal of Engineering, 1998, 20(5): 412-416. doi: 10.13374/j.issn1001-053x.1998.05.002

Discriminating Fractals In Time Series

doi: 10.13374/j.issn1001-053x.1998.05.002
  • Received Date: 1997-10-30
    Available Online: 2021-08-27
  • Fractal theory is a new method to apply in time series analysis, but how to discriminate fractal time series from non-fractal time series is ambiguous. Several Parame-ters, such as Poincare map, Lyapunov exponent, correlation dimension, power spectrum density and Hunt exponent, are used to recognize if the time series are fractals. The relia-bilities of the parameters used above arc compared. If the dynamic system is known, it's fit to use the Poincare map and Lyapunov exponent; while if the dynamic system is unknown, it, s fit to use the power spectrum density and Hurst exponent. At last, the range of fractals applying in time series analysis is sketched.

     

  • loading
  • 加載中

Catalog

    通訊作者: 陳斌, bchen63@163.com
    • 1. 

      沈陽化工大學材料科學與工程學院 沈陽 110142

    1. 本站搜索
    2. 百度學術搜索
    3. 萬方數據庫搜索
    4. CNKI搜索
    Article views (320) PDF downloads(27) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    久色视频