博碩士論文 etd-0220106-160055 詳細資訊


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姓名 李政聰(Cheng-Tsung Lee) 電子郵件信箱 m923010050@student.nsysu.edu.tw
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 94學年第1學期
論文名稱(中) 利用基因演算法產生模糊分類系統
論文名稱(英) Generation of Fuzzy Classification Systems using Genetic Algorithms
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    摘要(中) 為了解決樣本分類問題,本論文提出了一種改良式fuzzy GBML(genetic-based machine learning)演算法來設計一個FRBCS(fuzzy rule-based classification system)。
      過去的hybrid fuzzy GBML演算法使用了計算較為耗時的SS模糊模型,此外,為了加快匹茲堡式演算法的收斂速度,還結合了計算繁複的密西根式演算法,因而造成fuzzy GBML演算法的計算時間冗長。而我們所提出的改良式fuzzy GBML演算法使用了計算較為快速的MW模糊模型,且以一個計算簡單的heuristic procedure來取代密西根式演算法的功能。實驗結果亦顯示,我們的改良式fuzzy GBML演算法不但較hybrid fuzzy GBML演算法計算時間更短、收斂速度更快,且還提升了些微的分類正確率。
    摘要(英) In this thesis, we propose an improved fuzzy GBML(genetic-based machine learning)algorithm to construct a FRBCS(fuzzy rule-based classification system)for pattern classification problem.
      Existing hybrid fuzzy GBML algorithm is consuming more computational time since it used the SS fuzzy model and combined with the Michigan-style algorithm for increasing the convergent rate of the Pittsburgh-style algorithm. By contrast, our improved fuzzy GBML algorithm is consuming less computational time since it used the MW fuzzy model and instead of the role of the Michigan-style algorithm by a heuristic procedure. Experimental results show that improved fuzzy GBML algorithm possesses the shorter computational time, the faster convergent rate, and the slightly better classification rate.
    關鍵字(中)
  • 樣本分類
  • 模糊分類系統
  • fuzzy GBML
  • 模糊模型
  • 基因演算法
  • 關鍵字(英)
  • pattern classification
  • FRBCS
  • fuzzy GBML
  • fuzzy model
  • genetic algorithm
  • 論文目次 摘要..................................................i
    Abstract.............................................ii
    第一章 簡介.........................................1
    1.1 樣本分類問題.................................1
    1.2 學習能力與推廣能力...........................1
    1.3 編排方式.....................................3
    第二章 Fuzzy GBML演算法.............................4
    2.1 Fuzzy Rule-Based System......................4
    2.2 Fuzzy GBML演算法.............................7
    2.3 匹茲堡方法與密西根方法.......................8
    第三章 Hybrid Fuzzy GBML演算法.....................12
    3.1 模糊分割的方式與Fuzzy Rule的形式............13
    3.2 Fuzzy Rules的前提部份與結論部份.............15
    3.3 Single Winner Rule的模糊推論方法............18
    3.4 Heuristic Procedures........................20
    3.4.1 利用訓練樣本產生初始的Fuzzy Rules...........20
    3.4.2 利用被錯誤的分類或是拒絕分類的訓練樣本產生新的Fuzzy Rules..........................................22
    3.5 基因運算方式................................23
    3.6 Hybrid Fuzzy GBML演算法.....................25
    第四章 我們的方法..................................27
    4.1 動機........................................27
    4.2 Fuzzy Rule的形式及產生結論部份的方式........28
    4.3 Weighted Voting的模糊推論方法...............30
    4.4 過程及計算時間的比較........................31
    4.4.1 Fuzzy Rule產生結論部份的過程................31
    4.4.2 模糊推論過程................................32
    4.4.3 計算時間的比較..............................34
    4.5 改變模糊模型................................34
    4.5.I 信用指派的問題..............................35
    4.5.II 密西根式演算法存在的必要性..................36
    4.6 改良式Fuzzy GBML演算法......................38
    第五章 實驗結果與分析..............................42
    5.1 產生結論部份及模糊推論的時間比較............44
    5.2 改變模糊模型之後的因應方法..................45
    5.3 分類正確率與計算時間的比較..................47
    5.4 收斂速度的比較..............................48
    第六章 結論........................................53
    參考文獻.............................................54
    參考文獻 [1]. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol.8, pp.338-353, 1965.
    [2]. L. X. Wang, a Course in Fuzzy Systems and Control. Prentice Hall, 1997.
    [3]. L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Transactions on Systems, Man, and Cybernetics, vol.22, no.6, pp.1414-1427, 1992.
    [4]. J. S. R. Jang, “Self-learning fuzzy controllers based on temporal back propagation,” IEEE Transactions on Neural Networks, vol.3, no.5, pp.714-723, 1992.
    [5]. M. Grabisch and F. Dispot, “A comparison of some methods of fuzzy classification on real data,” in Proc. 2nd Int. Conf. Fuzzy Logic Neural Networks, Iizuka, Japan, July 1992, pp.659-662.
    [6]. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
    [7]. J. H. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
    [8]. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, 1989.
    [9]. P. Thrift, “Fuzzy logic synthesis with genetic algorithms,” in Proc. 4th Int. Conf. Genetic Algorithms, University of California, San Diego, July 13-16, 1991, pp.509-513.
    [10]. H. Nomura, I. Hayashi, and N. Wakami, “A self-tuning method of fuzzy reasoning by genetic algorithm,” in Proc. of the Int. Fuzzy Systems Intelligent Control Conf., Louisville, KY, Mar. 16-18,1992, pp.236-245.
    [11]. M. Valenzuela-Rendon, “The fuzzy classifier system: a classifier system for continuously varying variables,” in Proc. 4th Int. Conf. Genetic Algorithms, University of California, San Diego, July 13-16, 1991, pp.346-353.
    [12]. C. Z. Janikow, “A genetic algorithm for optimizing fuzzy decision trees,” in Proc. 6th Int. Conf. Genetic Algorithms, University Pittsburgh, Pittsburgh, PA, July 15-19, 1995, pp.421-428.
    [13]. Y. Yuan and H. Zhuang, “A genetic algorithm for generating fuzzy classification rules,” Fuzzy Sets System, vol.84, no.1, pp.1-19, Nov. 1996.
    [14]. S. Abe and M. -S. Lan, “A method for fuzzy rules extraction directly from numerical data and its application to pattern classification,” IEEE Transactions on Fuzzy Systems, vol.3, no.1, pp.18-28, Feb. 1995.
    [15]. S. F. Smith, “A learning system based on genetic algorithms,” Ph.D. dissertation, University Pittsburgh, Pittsburgh, PA, 1980.
    [16]. L. B. Booker, D. E. Goldberg, and J. H. Holland, “Classifier systems and genetic algorithms,” Artificial Intelligence, vol.40, pp.235-282, Sep. 1989.
    [17]. H. Ishibuchi, T. Yamamoto, and T. Nakashima, “Hybridization of fuzzy GBML approaches for pattern classification problems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol.35, no.2, pp.359-365, Apr. 2005.
    [18]. H. Ishibuchi, T. Nakashima, and T. Murata, “Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol.29, no.5, pp.601-618, Oct. 1999.
    [19]. H. Ishibuchi and T. Nakashima, “Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes,” IEEE Transactions on Industrial Electronics, vol.46, no.6, pp.1057-1068, Dec. 1999.
    [20]. H. Ishibuchi and T. Yamamoto, “Rule weight specification in fuzzy rule-based classification systems,” IEEE Transactions on Fuzzy Systems, vol.13, no.4, pp.428-435, Aug. 2005.
    [21]. O. Cordon, F. Herrera, F. Hoffman, and L. Magdalena, Genetic Fuzzy Systems. Singapore: World Scientific, 2001.
    口試委員
  • 黃宗傳 - 召集委員
  • 吳志宏 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2006-01-13 繳交日期 2006-02-20

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