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    題名: 時間數列分析在偵測型態結構差異上之探討
    Application Of Time Series Analysis In Pattern Recgnition And alysis
    作者: 蘇曉楓
    Su, Shiau Feng
    貢獻者: 吳柏林
    Wu, Berlin
    蘇曉楓
    Su, Shiau Feng
    關鍵詞: 非線性時間數列模式
    神經網路
    穩健性
    模型辨識
    時間數列分析
    nonlinear time series
    neural
    time series analysis
    日期: 1993
    上傳時間: 2016-04-29 16:43:56 (UTC+8)
    摘要: 依時間順序出現之一連串觀測值，通常會呈現某一型態，而根據所產生的型態可以作為判斷事件發生的基礎。例如，震波形成原因的判斷﹔追查環境污染源﹔以及在醫學方面，辨識一個正常人心電圖的型態與患有心臟病的病人其心電圖的型態…等。對於這些問題，傳統之辨識方法常因前提假設的限制而失去其準確性。在本文中，我們應用神經網路中的逆向傳播演算法則來訓練網路，並利用此受過訓練的網路來辨別線性時間數列ARIMA及非線性時間數列 BL(1,0,1,1)。結果發現，網路對於模擬資料中雙線性係數介於0.2至$0.8$之間的資料有高達$80\\%$以上的辨識能力。而在實例研究中，我們訓練網路來判斷震波形成的原因，其正確率亦高達80\\%以上。同時，我們也將神經網路應用在環境保護方面，訓練網路來判斷二地區空氣品質的型態。
    A series of observations indexed in time often produces a
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    描述: 碩士
    國立政治大學
    統計學系
    G80354009
    資料來源: http://thesis.lib.nccu.edu.tw/record/#B2002004195
    資料類型: thesis
    顯示於類別:[統計學系] 學位論文

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