English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 80990/80990 (100%)
造訪人次 : 42098401      線上人數 : 653
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋


    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/69433


    題名: Convergent Cross Mapping (CCM) 方法對預測因果關係的評估;Assessment of Convergent Cross Mapping (CCM) Method by Using time-series data of Known Causality
    作者: 陳學金;Ting,Hock-King
    貢獻者: 物理學系
    關鍵詞: 因果關係;Convergent Cross Mapping;CCM;causality
    日期: 2016-01-28
    上傳時間: 2016-03-17 20:37:29 (UTC+8)
    出版者: 國立中央大學
    摘要: 在許多不同的研究領域,揭開兩個物理量X和Y之間的因果關係
    是一項重要的工作。X→Y表示X影響Y。Convergent Cross
    Mapping (CCM) 是一個檢測因果關係的方法。這論文在介紹了
    CCM 的運作原理之後,我們將使用兩個已知因果關係的時間序列
    X(t)和Y(t) 去對CCM 進行評估,且 X(t)和Y(t) 具有相同或不同
    (即混沌、週期振盪以及穩定點) 的動力學行為。當X(t)和Y(t) 同
    步的時候,CCM 無法區分 X和Y 的因果關係。最後,這論文將討
    論透過三個節點和一百個節點的環網絡對CCM 所進行的評估結果。;In many different areas of research, it is important to uncover the interaction or causal relation between two dynamical quantities (such as X(t) and Y (t)). If X is the cause and Y is the effect, then X will influence/drive Y . Such a causality or directed interaction can be denoted as: X drives Y . Convergent Cross Mapping (CCM) is a method used to detect the causality between X and Y .
    In this thesis, the working principles behind CCM (i.e. state space reconstruction and cross estimation) will first be introduced. Then, the CCM method is assessed by using it to detect the causality of X and Y , in which they were generated by solving a system of coupled dynamical equations numerically. Since X(t) and Y(t) are two time series of known causality (e.g. X drives Y), the accuracy of the CCM method could be assessed by plotting sigma(X drives Y) versus gXY and sigma(Y drives X) versus gXY on the same graph, where sigma is a CCM accuracy indicator and gXY is the
    coupling strength of X drives Y. For further assessment, CCM method is applied to detect the causality of X and Y of different combinations of dynamical behaviours (i.e. chaos, periodic oscillations and stable fixed point). It was found that when X(t) and Y(t) synchronize, CCM is unable to distinguish the true causality from the non-existing causality.
    From the 3-node (X drives Y drives Z) motifs analysis, it was found that CCM method would misinterpret the existence of X drives Z, when both X and Y synchronize. Apart from that, the existence of the conflicting information would also affect the sigma value computed by the CCM method.
    Finally, the sigma versus g curves for 2-node and 100-node (ring network) of different connectivities and ranges of g are plotted on the same graph. This is to investigate the universality of the sigma versus g relation. It was found that the true causality curve of 2-node unidirectional case fits the Power Law: sigma proportional to g^(-0.6). There
    are a number of cases of points which fall on or close to the true causality curve of 2-node unidirectional case. The deviations of the rests of the points from the true causality curve are either due to the A drives B drives C effect or the conflicting information problem or both.
    顯示於類別:[物理研究所] 博碩士論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML540檢視/開啟


    在NCUIR中所有的資料項目都受到原著作權保護.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明