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    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/81032


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    題名: On the Optimization Methods for Fully Fuzzy Regression Models
    作者: Wu, Berlin
    吳柏林
    Liu, Hsuan-Ku
    Hsu, Yu-Yun
    貢獻者: 應數系
    關鍵詞: Fuzzy regression;Fuzzy parameter;H-cut;Methods of least square;Triangular membership function
    日期: 2010-03
    上傳時間: 2016-02-01 16:07:15 (UTC+8)
    摘要: The goal of this paper is to construct a multiple fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. We propose an optimization model that provides fuzzy coefficients to minimize the distance between the fuzzy regressands and the fuzzy regressors. It concerned with imprecise measurement of observed variables, linear programming estimation and non-parametric methods. This is different from the assumptions as well as the estimation techniques of the classical analysis. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional regression analysis did.
    關聯: International Journal of Intelligent Technologies & Applied Statistics, 3(1), 45-55
    資料類型: article
    顯示於類別:[應用數學系] 期刊論文

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