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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/49459
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/49459


    Title: 模糊資料分類與模式建構探討-以單身人口數及失業率為例
    A study on the fuzzy data classification and model construction - with case study on the population of singles versus unemployment rate
    Authors: 游鈞毅
    Yu,Chun Yi
    Contributors: 吳柏林
    Wu,Berlin
    游鈞毅
    Yu,Chun Yi
    Keywords: 模糊資料分類
    轉折區間
    平均累加模糊熵
    失業率
    單身人口數
    fuzzy data classification
    average of the sum of fuzzy entropies
    change periods
    unemployment rate
    population of singles
    Date: 2009
    Issue Date: 2010-12-08 11:52:51 (UTC+8)
    Abstract: 資料分類的應用在時間數列的分析與預測過程相當重要。而模糊資料近年來更受到重視,其應用的範圍包含:財金、社會、生醫、電機等各個領域。本研究欲運用模糊資料分類法,對區間時間數列的轉折偵測與模式建構做一個深入探討。主要應用平均累加模糊熵(average of the sum of fuzzy entropies), 找出其結構性改變的區間。並針對區間型時間數列進行模式建構診斷與預測。最後我們以單身人口數與失業率為實列做一個詳細的探討。結果顯示,失業率對單身人口數有顯著的影響而孤鸞年的效應並不顯著。
    The application of data classifications in time series analysis and forecasting is rather important. The fuzzy data classification has received much attention recently. It can be applied on various fields such as finance, sociology, biomedicine, electrical engineering and so on. This study is to use the fuzzy data classification to perform an intensive research on the change periods detection and model construction of the interval time series. We use average of the sum of fuzzy entropies to find out interval of the structural changes. Focusing on the time series of intervals, we build a model and make prediction about it. At the end, based on the case study on the population of singles versus, we thoroughly discuss this topic. The result shows that the unemployment rate does significantly correlate with the population of singles, but the "widow`s year" does not .
    Reference: 中文部分
    [1]吳柏林1995時間數列分析與導論 台北 華泰書局.
    [2]吳柏林2005 模糊統計導論方法與應用 台北 五南書局.
    [3]吳柏林1999 模糊統計分類在臺灣地區失業率分析與預測之應用 中國統 計學報37:1,37-52.
    [4]黃士滔 2004台灣地區失業率預測分析 工程科技與教育刊 ,1:2,257-269.
    [5]胡愈寧2004 整合時間序列資料與總體經濟變數於失業率預測之應用 育達 學院學報,139-170.
    [6]許永河 1998台灣地區自然失業率之估計 成功大學學報,33,125-158.
    [7]劉浩天 2004非時變模糊時間數列預測模式之研究 管理研究學報,69-189.
    [8]楊靜利2006臺灣傳統婚配空間的變化與婚姻行為之變遷 人口學刊.33 ,1-32.
    [9]張弘紋2010應用模糊多屬性群體決策方法於研發專案之選擇 專案管理學刊,3:1,74-90.
    [10]陳嘉甄2009以模糊聚類方法分析數學錯誤概念組型例 教育研究與發展期
    刊,5:4,159-186.
    [11]林原宏2005模糊集群 教育研究,138,142-143.
    英文部分
    [1]Wu, B (1999). Use of fuzzy statistical technique in change period detection of nonlinear time series. Applied Mathematics and Computation,99, 241-254.
    [2] Y. Yoshinari, W. Pedrycz, K. Hirota.(1993) Construction of fuzzy models through clustering techniques,Fuzzy Sets and Systems, 54, 157-165.
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    [4] Jiulun Fan and Weixin Xie.(1999) Distance measure and induced fuzzy entropy, Fuzzy Sets and Systems,104:2 ,305-314.
    [5] Ioannis K. Vlachos, George D.(2007) Sergiadis Subsethood, entropy, and cardinality for interval-valued fuzzy sets—An algebraic derivation Fuzzy Sets and Systems,158,1384-1396
    [6] Michael P. Windham (1981). Cluster validity for fuzzy clustering algorithms Fuzzy Sets and Systems, 5:2, 177-185
    [7] Andrews, D. W. K. and Ploberger, W.(1994) Optimal tests when a nuisance parameter is present only under the alternative, Econometrica, 62(6), 1383-1414.
    [8] Bai, J. and Perron, P. (1998), Estimating and testing linear models with multiple structural changes, Econometrica, 66, 47–78.
    [9] Kumar, K. and Wu, B. (2001), Detection of change points in time series analysis with fuzzy statistics,International Journal of Systems Science, 32(9), 1185-1192.
    [10] Zhou, H. D. (2005), Nonlinearity or structural break? - data mining in evolving financial data sets from a Bayesian model combination perspective,Proceedings of the 38th Hawaii International Conference on System Sciences.
    [11]Chow,G.C. (1960). Testing for equality between sets of coefficients in two linear regression. Econometrics,28,291-260.
    [12] San, O.M., Huynh, V., and Nakamori, Y. (2004), An alternative extension of the K-means algorithm for clustering categorical data, Int. J. Appl. Math. Comput. Sci,14:2, 241-247.
    [13] Weina Wang, Yunjie Zhanga(2007). On fuzzy cluster validity indices, Fuzzy Sets and Systems ,58,2095–2117.
    [14] Malay K. Pakhira, Sanghamitra Bandyopadhyay, Ujjwal Maulik(2005). A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets and Systems, 15:2, 191-214.
    [15] Dae-Won Kim, Kwang H. Lee, Doheon Lee(2004). On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recognition, 37: 10, 2009-2025
    [16] Kuo-Lung Wu, Miin-Shen Yang(2005). A cluster validity index for fuzzy clustering.Pattern Recognition Letters,26:9,1275-1291.
    [17] Gin-Shuh Liang, Tsung-Yu Chou, Tzeu-Chen Han(2005). Cluster analysis based on fuzzy equivalence relation,European Journal of Operational Research, 166: 1,160-171.
    [18] Wenyi Zeng, Hongxing Li(2006). Relationship between similarity measure and entropy of interval valued fuzzy sets.Fuzzy Sets and Systems, 157: 11, 1477-1484.
    [19]Jia-Chun Xie.Berlin Wu.Songsak Sriboonchita(2010). Fuzzy Estimation Methods and their Application in Real Estimation Evaluation.Internation Journal of Intelligent technique and application statistics.(will application)
    Description: 碩士
    國立政治大學
    應用數學研究所
    97751006
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097751006
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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