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題名 模糊數據的局部加權回歸
Locally weighted regression of fuzzy data作者 陳帥 貢獻者 吳柏林
陳帥關鍵詞 模糊理論
模糊回歸分析
局部加權
Fuzzy theory
Fuzzy regression
Locally weighted method日期 2017 上傳時間 3-Jul-2017 14:41:05 (UTC+8) 摘要 目標：本文旨在建構一種新型的模糊回歸模式，解決一类較複雜的模糊回歸問題。研究方法:推廣局部加權回歸的思想，先從理論上構建新模型；然後借由模拟數據，從多個方面考察新模型的性質，并和其他模型做比較。發現：局部加權回歸方法結合模糊隸屬度概念，使模糊回歸理論有更多的應用場合。原創性：目前在模糊回歸領域的主流思想是通過線性規劃等方法來構建模型，而本文另闢蹊徑，首次從局部加權的角度構建了模糊回歸的新模型。
Objective: This paper aims to construct a new fuzzy regression model to solve a more complex fuzzy regression problem.Method: Build a new model by promoting the idea of locally weighted regression; Using simulated data to compare the new model with other models.Conclusion: The fuzzy membership degree concept combined with the locally weighted regression method makes the fuzzy regression theory have more applications.Originality: At present, the main idea in the field of fuzzy regression is to construct models by means of linear programming. In this paper, a new model of fuzzy regression is constructed from the perspective of locally weighted method for the first time.參考文獻 [1] L.A. Zadeh, Fuzzy sets, Information and Control, Volume 8, Issue 3, June 1965, pp.338–353[2] H. Tanaka, S. Uejima, K. Asai，Linear regression analysis with fuzzy model， IEEE Trans. Sys., Man. Cyber., 12 (1982), pp. 903–907.[5] William S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, Vol. 74,No. 368.(Dec., 1979),pp. 829-836.[6]Phil Diamond, Fuzzy Least Squares, Information Sciences 46(3), 1988, pp.141-157[7] Pierpaolo D`Urso, Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data ,Computational Statistics & Data Analysis, Volume 42, Issues 1–2, (2003), pp.47–72.[8] P. Anand Raj, D. Nagesh Kumar, Ranking alternatives with fuzzy weights using maximizing set and minimizing set ,Fuzzy Sets and Systems,1999,pp365-375[3]吳柏林，模糊統計導論第二版（2015），五南出版社（台北），p153.[4]陳孝煒、吳柏林，區間回歸與模糊樣本分析，管理科學與統計決策, 4(1), 2007 描述 碩士
國立政治大學
應用數學系
104751018資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104751018 資料類型 thesis dc.contributor.advisor 吳柏林 zh_TW dc.contributor.author (Authors) 陳帥 zh_TW dc.creator (作者) 陳帥 zh_TW dc.date (日期) 2017 en_US dc.date.accessioned 3-Jul-2017 14:41:05 (UTC+8) - dc.date.available 3-Jul-2017 14:41:05 (UTC+8) - dc.date.issued (上傳時間) 3-Jul-2017 14:41:05 (UTC+8) - dc.identifier (Other Identifiers) G0104751018 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110693 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 應用數學系 zh_TW dc.description (描述) 104751018 zh_TW dc.description.abstract (摘要) 目標：本文旨在建構一種新型的模糊回歸模式，解決一类較複雜的模糊回歸問題。研究方法:推廣局部加權回歸的思想，先從理論上構建新模型；然後借由模拟數據，從多個方面考察新模型的性質，并和其他模型做比較。發現：局部加權回歸方法結合模糊隸屬度概念，使模糊回歸理論有更多的應用場合。原創性：目前在模糊回歸領域的主流思想是通過線性規劃等方法來構建模型，而本文另闢蹊徑，首次從局部加權的角度構建了模糊回歸的新模型。 zh_TW dc.description.abstract (摘要) Objective: This paper aims to construct a new fuzzy regression model to solve a more complex fuzzy regression problem.Method: Build a new model by promoting the idea of locally weighted regression; Using simulated data to compare the new model with other models.Conclusion: The fuzzy membership degree concept combined with the locally weighted regression method makes the fuzzy regression theory have more applications.Originality: At present, the main idea in the field of fuzzy regression is to construct models by means of linear programming. In this paper, a new model of fuzzy regression is constructed from the perspective of locally weighted method for the first time. en_US dc.description.tableofcontents 1.前言 12．模糊數據的局部加權回歸 52.1 模型的建構 52.2 回歸係數的估計 62.3 殘差分析 72.4 數據模擬 83.實證分析 124.結語 18參考文獻 19 zh_TW dc.format.extent 1396648 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104751018 en_US dc.subject (關鍵詞) 模糊理論 zh_TW dc.subject (關鍵詞) 模糊回歸分析 zh_TW dc.subject (關鍵詞) 局部加權 zh_TW dc.subject (關鍵詞) Fuzzy theory en_US dc.subject (關鍵詞) Fuzzy regression en_US dc.subject (關鍵詞) Locally weighted method en_US dc.title (題名) 模糊數據的局部加權回歸 zh_TW dc.title (題名) Locally weighted regression of fuzzy data en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] L.A. Zadeh, Fuzzy sets, Information and Control, Volume 8, Issue 3, June 1965, pp.338–353[2] H. Tanaka, S. Uejima, K. Asai，Linear regression analysis with fuzzy model， IEEE Trans. Sys., Man. Cyber., 12 (1982), pp. 903–907.[5] William S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, Vol. 74,No. 368.(Dec., 1979),pp. 829-836.[6]Phil Diamond, Fuzzy Least Squares, Information Sciences 46(3), 1988, pp.141-157[7] Pierpaolo D`Urso, Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data ,Computational Statistics & Data Analysis, Volume 42, Issues 1–2, (2003), pp.47–72.[8] P. Anand Raj, D. Nagesh Kumar, Ranking alternatives with fuzzy weights using maximizing set and minimizing set ,Fuzzy Sets and Systems,1999,pp365-375[3]吳柏林，模糊統計導論第二版（2015），五南出版社（台北），p153.[4]陳孝煒、吳柏林，區間回歸與模糊樣本分析，管理科學與統計決策, 4(1), 2007 zh_TW
