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。

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.

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

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