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題名 模糊統計分類及其在茶葉品質評定的應用
Analysis fuzzy statistical cluster and its application in tea quality
作者 林雅慧
Lin, Ya-Hui
貢獻者 吳柏林<br>謝邦昌
Wu, Berlin<br>Shia, Ben-Chang
林雅慧
Lin, Ya-Hui
關鍵詞 群落分析
隸屬度函數
模糊評鑑表
模糊權重
加權模糊分類法
Cluster analysis
Membership function
Fuzzy judgement table
Fuzzy weight
Weighted fuzzy clustering method
日期 1996
上傳時間 28-Apr-2016 11:48:33 (UTC+8)
摘要 模糊理論開始於 1960 年代中期,關於這方面的研究與發展均已獲得相當不錯的成果.其中尤以在群落分析應用上的專題研究更是廣泛.Bezdek 提出的模糊分類演算法,乃根據 Dunn 的C平均法所作的一改良方法.但仍有其缺點,例如,未考慮權重且以靜態資料為主. 有鑑於此,本研究對 Bezdek 之方法加以改進推廣,提出加權模糊分類法.對於評價因素為多變量時,應加入模糊權重的考量.此外更結合時間因素,使準則函數成為動態的模式,將傳統的模糊分類法由靜態資料轉為動態資料形式,以反映真實
Research on the theory of fuzzy sets has been growing steadily since itsinception during the mid-1960s. The literature especially dealing with fuzzycluster analysis is quite extensive. But the research on FCM still has somedisadvantages. For instance, the
參考文獻 吳柏林(1994) 時間數列的圓形識別與分類。國立編譯館館刊，第23卷，第2期，頁317-344 。
吳振鐸(1964) 茶葉品質鑑定與茶湯中總多元的類及花青素含量之相關研究。平鎮:茶葉試驗所報告第19號。
吳振鐸(1973) 從茶湯之化學成分談台灣茶葉品質之改進。台灣農業季刊，第9卷，第1期，頁194-198。
陳國任、謝邦昌(1996) 集群分析在製茶品質分類之應用。台灣茶業研究彙報，(付印中) 。
Bezdek, J. C. (1972) Feature selection for binary data - medical diagnosis with fuzzy sets. in Proc. Nat. Comput Conf , AFIPS Press , pp. 1057-1068.
Bezdek, J. C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum.
Bezdek, J. C. (1983) Some recent application of fuzzy c-means in pattern recognition and image processing. IEEE 1983 Workshop 011 Languages for Automatioin, IEEE Computer Society, pp . 247-252.
Bezdek, J. C. (1983) Statistical pattern recognition systems. Boeing Aerospace. Kent . WA, Tech. Rep. TR-SC8300 1-004.
Bezdek, J. C. and Solomon, K. (1981) Simulation of implicit numerical characteristics using small samples. in Proc. ICASRC 6. New York : Pergamon, pp. 2773 -2784 .
Bezdek, J. C. et. al. (1981) Fuzzy clustering g: a new approach for geostatistical analysis . Int. J. Syst. Meas. Decision , Vol. 1, pp. 13 -23 .
Calbert, M. Y. and Merlushkin, A. I. (1994) Evaluating the parameter based on a contaminated sample. Nauchno-Tekhnicheskaya Informatsiya, Serial 2, 28 , 26-36.
Cannon, R. L. and .Jacobs, C. (1984) Multispectral pixed classification With fuzzy objective functions . Cen. for Automation Res. , Univ. Maryland , College Park, Tech. Rep CAR-TR-51.
Cannon, R. L. , Dave, J . V. and Bezdek, J. C. (1986) Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. PAMI -8, No.2, pp . 248-255.
Dunn, J. C. (1974) A fuzzy relative of the ISODATA process and its use in detecting compact, well -separated clusters . J. Cybern ., Vol. 3, pp. 32-57.
Harris, T. R. , Stoddard, S. W. and Bezdek, J. C. (1993) Application of fuzzy-set clustering for regional typologies . Growth and Change, Vol. 24, pp . 155 - 165 .
Huggins, V. J. (1983) Computing Maximum-Likelihood Estimates for Normal Mixtures. University of South Carolina . M. S. Thesis
Kissiov, V. T. and Hadjitodorov, S. T. (1992) A fuzzy version of K-NN method. Fuzzy Sets and Systems, Vol. 49, pp . 323-329.
Klir, G. J. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic - Theory and Applications. Prentice-Hall , Upper Saddle River, NJ.
Manski , C. F. (1990) The use of intentions data to predict behavior: a best-case analysis. JASA , Vol. 85, No. 412, pp. 934-940.
Mukherjee, D. P. et. al. (1995) Water quality analysis: a pattern recognition approach. Pattern Recognition, Vol. 28, No.2, pp. 269-281 .
Narazaki , H. and Ralescu, A. L. (1994) Iterative induction of a category membership function. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , Vol. 2, No. 1, pp . 91-100 .
Pal, N. R. and Bezdek, J. C. (1995) On cluster validity for the fuzzy c-mea ns model, IEEE Trans. Fuzzy Systems, Vol. 3, No.3 , pp. 370-379 .
Park, K. S. and Kim, S. H. (1996) A note on the fuzzy weighted additive rule. Fuzzy Sets and Systems, Vol. 77 , pp . 315-320 .
Ramakrishnan, R. and Rao, C. J. M. (1992) The fuzzy weighted additive rule. Fuzzy Sets and Systems, Vol. 46, pp. 177- 187.
Roberge, P. R. (199 5) A knowledge-based framework for planning non-destructive evaluation . Expert Systems, Vol. 12, No. 2, pp . 10 7- 11 2.
Romer, C. and Kandel, A. and Backer, E. (1995) Fuzzy partitions of the sample space and fuzzy parameter hypotheses. IEEE Transs. Systems, Man and Cybernet., Vol. 25 , No.9, pp . 13 14- 132 1.
Ross , T. J. (199 5) Fuzzy Logic with Engineering Applications.
Sato, M. and Sato, Y. ( 1994) On a multi criteria fuzzy clustering method for 3-way data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 2, pp. 127-142.
Uhrmacher, A. M. (1995) Reasoning about changing structure: a modeling concept for ecological systems . Applied Artificial Intelligence , Vol. 9, pp . 157- 180.
Wang, H. and Bell , P. M. (1996) Fuzzy clustering analysis and multifactorial evaluation for students` imaginative power in physics problem solving. Fuzzy Sets and Systems, Vol. 78 , pp . 95- 105.
Watanabe, N. and- Imaizumi , T. (1993) A fuzzy statistical test of fuzzy hypotheses. Fuzzy Sets and Systems, Vol. 53 , pp. 167-178.
Windham, M. P. (1 982) Cluster validity for the fuzzy c-means clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. PAMI-4, No. 4, pp. 357-363.
Zadeh, L. A. (1965) Fuzzy sets. Information and Control, Vol. 8, pp . 338-353.
Zenovich, S. V. (1994) Integration of classification structures. Nauchno-Tekhnicheskaya Informatsiya , Seriya 2, Vol. 28 , No.6, pp. 8-12.
描述 碩士
國立政治大學
統計學系
83354007
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002002790
資料類型 thesis
dc.contributor.advisor 吳柏林<br>謝邦昌zh_TW
dc.contributor.advisor Wu, Berlin<br>Shia, Ben-Changen_US
dc.contributor.author (Authors) 林雅慧zh_TW
dc.contributor.author (Authors) Lin, Ya-Huien_US
dc.creator (作者) 林雅慧zh_TW
dc.creator (作者) Lin, Ya-Huien_US
dc.date (日期) 1996en_US
dc.date.accessioned 28-Apr-2016 11:48:33 (UTC+8)-
dc.date.available 28-Apr-2016 11:48:33 (UTC+8)-
dc.date.issued (上傳時間) 28-Apr-2016 11:48:33 (UTC+8)-
dc.identifier (Other Identifiers) B2002002790en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/87304-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 83354007zh_TW
dc.description.abstract (摘要) 模糊理論開始於 1960 年代中期,關於這方面的研究與發展均已獲得相當不錯的成果.其中尤以在群落分析應用上的專題研究更是廣泛.Bezdek 提出的模糊分類演算法,乃根據 Dunn 的C平均法所作的一改良方法.但仍有其缺點,例如,未考慮權重且以靜態資料為主. 有鑑於此,本研究對 Bezdek 之方法加以改進推廣,提出加權模糊分類法.對於評價因素為多變量時,應加入模糊權重的考量.此外更結合時間因素,使準則函數成為動態的模式,將傳統的模糊分類法由靜態資料轉為動態資料形式,以反映真實zh_TW
dc.description.abstract (摘要) Research on the theory of fuzzy sets has been growing steadily since itsinception during the mid-1960s. The literature especially dealing with fuzzycluster analysis is quite extensive. But the research on FCM still has somedisadvantages. For instance, theen_US
dc.description.tableofcontents 壹、前言..........1
貳、模糊測度(fuzzy measure)與分類方法..........3
2.1傳統分類法..........3
2.2模糊分類法..........5
2.3類神經網路分類法..........7
參、模糊判定程序..........9
3.1模糊權重分析..........9
3.2加權模糊分類..........15
3.3模糊統計分類的評定..........20
3.4綜合比較..........22
肆、模擬與實證分析..........23
4.1模擬結果與分析..........23
4.2實證分析－以茶葉資料為例..........28
伍、結論..........34
參考文獻..........35
附表..........39		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002002790en_US
dc.subject (關鍵詞) 群落分析zh_TW
dc.subject (關鍵詞) 隸屬度函數zh_TW
dc.subject (關鍵詞) 模糊評鑑表zh_TW
dc.subject (關鍵詞) 模糊權重zh_TW
dc.subject (關鍵詞) 加權模糊分類法zh_TW
dc.subject (關鍵詞) Cluster analysisen_US
dc.subject (關鍵詞) Membership functionen_US
dc.subject (關鍵詞) Fuzzy judgement tableen_US
dc.subject (關鍵詞) Fuzzy weighten_US
dc.subject (關鍵詞) Weighted fuzzy clustering methoden_US
dc.title (題名) 模糊統計分類及其在茶葉品質評定的應用zh_TW
dc.title (題名) Analysis fuzzy statistical cluster and its application in tea qualityen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 吳柏林(1994) 時間數列的圓形識別與分類。國立編譯館館刊，第23卷，第2期，頁317-344 。
吳振鐸(1964) 茶葉品質鑑定與茶湯中總多元的類及花青素含量之相關研究。平鎮:茶葉試驗所報告第19號。
吳振鐸(1973) 從茶湯之化學成分談台灣茶葉品質之改進。台灣農業季刊，第9卷，第1期，頁194-198。
陳國任、謝邦昌(1996) 集群分析在製茶品質分類之應用。台灣茶業研究彙報，(付印中) 。
Bezdek, J. C. (1972) Feature selection for binary data - medical diagnosis with fuzzy sets. in Proc. Nat. Comput Conf , AFIPS Press , pp. 1057-1068.
Bezdek, J. C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum.
Bezdek, J. C. (1983) Some recent application of fuzzy c-means in pattern recognition and image processing. IEEE 1983 Workshop 011 Languages for Automatioin, IEEE Computer Society, pp . 247-252.
Bezdek, J. C. (1983) Statistical pattern recognition systems. Boeing Aerospace. Kent . WA, Tech. Rep. TR-SC8300 1-004.
Bezdek, J. C. and Solomon, K. (1981) Simulation of implicit numerical characteristics using small samples. in Proc. ICASRC 6. New York : Pergamon, pp. 2773 -2784 .
Bezdek, J. C. et. al. (1981) Fuzzy clustering g: a new approach for geostatistical analysis . Int. J. Syst. Meas. Decision , Vol. 1, pp. 13 -23 .
Calbert, M. Y. and Merlushkin, A. I. (1994) Evaluating the parameter based on a contaminated sample. Nauchno-Tekhnicheskaya Informatsiya, Serial 2, 28 , 26-36.
Cannon, R. L. and .Jacobs, C. (1984) Multispectral pixed classification With fuzzy objective functions . Cen. for Automation Res. , Univ. Maryland , College Park, Tech. Rep CAR-TR-51.
Cannon, R. L. , Dave, J . V. and Bezdek, J. C. (1986) Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. PAMI -8, No.2, pp . 248-255.
Dunn, J. C. (1974) A fuzzy relative of the ISODATA process and its use in detecting compact, well -separated clusters . J. Cybern ., Vol. 3, pp. 32-57.
Harris, T. R. , Stoddard, S. W. and Bezdek, J. C. (1993) Application of fuzzy-set clustering for regional typologies . Growth and Change, Vol. 24, pp . 155 - 165 .
Huggins, V. J. (1983) Computing Maximum-Likelihood Estimates for Normal Mixtures. University of South Carolina . M. S. Thesis
Kissiov, V. T. and Hadjitodorov, S. T. (1992) A fuzzy version of K-NN method. Fuzzy Sets and Systems, Vol. 49, pp . 323-329.
Klir, G. J. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic - Theory and Applications. Prentice-Hall , Upper Saddle River, NJ.
Manski , C. F. (1990) The use of intentions data to predict behavior: a best-case analysis. JASA , Vol. 85, No. 412, pp. 934-940.
Mukherjee, D. P. et. al. (1995) Water quality analysis: a pattern recognition approach. Pattern Recognition, Vol. 28, No.2, pp. 269-281 .
Narazaki , H. and Ralescu, A. L. (1994) Iterative induction of a category membership function. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , Vol. 2, No. 1, pp . 91-100 .
Pal, N. R. and Bezdek, J. C. (1995) On cluster validity for the fuzzy c-mea ns model, IEEE Trans. Fuzzy Systems, Vol. 3, No.3 , pp. 370-379 .
Park, K. S. and Kim, S. H. (1996) A note on the fuzzy weighted additive rule. Fuzzy Sets and Systems, Vol. 77 , pp . 315-320 .
Ramakrishnan, R. and Rao, C. J. M. (1992) The fuzzy weighted additive rule. Fuzzy Sets and Systems, Vol. 46, pp. 177- 187.
Roberge, P. R. (199 5) A knowledge-based framework for planning non-destructive evaluation . Expert Systems, Vol. 12, No. 2, pp . 10 7- 11 2.
Romer, C. and Kandel, A. and Backer, E. (1995) Fuzzy partitions of the sample space and fuzzy parameter hypotheses. IEEE Transs. Systems, Man and Cybernet., Vol. 25 , No.9, pp . 13 14- 132 1.
Ross , T. J. (199 5) Fuzzy Logic with Engineering Applications.
Sato, M. and Sato, Y. ( 1994) On a multi criteria fuzzy clustering method for 3-way data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 2, pp. 127-142.
Uhrmacher, A. M. (1995) Reasoning about changing structure: a modeling concept for ecological systems . Applied Artificial Intelligence , Vol. 9, pp . 157- 180.
Wang, H. and Bell , P. M. (1996) Fuzzy clustering analysis and multifactorial evaluation for students` imaginative power in physics problem solving. Fuzzy Sets and Systems, Vol. 78 , pp . 95- 105.
Watanabe, N. and- Imaizumi , T. (1993) A fuzzy statistical test of fuzzy hypotheses. Fuzzy Sets and Systems, Vol. 53 , pp. 167-178.
Windham, M. P. (1 982) Cluster validity for the fuzzy c-means clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. PAMI-4, No. 4, pp. 357-363.
Zadeh, L. A. (1965) Fuzzy sets. Information and Control, Vol. 8, pp . 338-353.
Zenovich, S. V. (1994) Integration of classification structures. Nauchno-Tekhnicheskaya Informatsiya , Seriya 2, Vol. 28 , No.6, pp. 8-12.		zh_TW