本論文使用非二進位制編碼及解碼、漸增突變機制、菁英政策及人口汰新政策來改良簡單型基因演算法，使得改良後的基因演算法在大範圍的參數搜尋時能降低因過早收斂而落入局部最佳解的可能性，並明顯提升搜尋到最佳參數的機會。
本研究利用SIRMs模糊控制器設計倒單擺系統，其中所建立的動態重要度數將使得連桿角度控制的優先權遠大於推車位移控制的優先權，倒單擺系統不會因為同時控制角度與推車位移而相對失去穩定度，並使用改良式基因演算法搜尋SIRMs模糊控制器的參數，由電腦模擬的結果可以看出，能達到快速並穩定的結果。

We use non-binary coding, elitist strategy, increasing mutation rate, extinction, and immigration strategy to improve the simple genetic algorithms in this study. We expect that the search technique can avoid falling into the local optimum due to the premature convergence, and purse the chance that finding the near-optimal parameters in the larger searching space could be obviously increased.
We utilize SIRMs(Single Input Rule Modules) fuzzy controller for the stabilization control of inverted pendulum systems, and the dynamic importance degrees are built such that the angular control of the pendulum takes priority over the position control of the cart. We utilize modified genetic algorithms(MGA) to automatically tuning scaling factors of SIRMs fuzzy controller. From computer simulations, the pendulum control and the cart position control can fastly be stabilized.

目錄………………………………………………………………….….Ⅰ
圖目錄…………………………………………………………………..Ⅴ
表目錄………………………………………………………………......Ⅶ
中文摘要…………………………………………………….……...…..Ⅷ
English Abstract…………………………………………………………Ⅸ

第一章 緒論
1.1 研究動機 1
1.2 研究目的 1
1.3 文獻回顧 1
1.3.1 基因演算法 1
1.3.2 SIRMs模糊控制 2
1.4 研究成果與貢獻 3
1.5 論文架構 4

第二章 基因演算法 5
2.1 前言 5
2.2 基因演算法基本架構 7
2.2.1 資料結構 7
2.2.2 編碼與解碼程序 8
2.2.3 定義適合度函數 9
2.2.4 適合度調整 10
2.2.5 產生初始族群 12
2.3 簡單型基因演算法 13
2.4 改良式基因演算法 15
2.4.1 複製 16
2.4.2 交配 16
2.4.3 突變 19
2.4.4 菁英政策 20
2.4.5 人口汰新政策 21
2.4.6 改良式基因演算法架構 23

第三章 模糊系統 24
3.1 模糊理論的沿革 24
3.2 模糊集合與歸屬函數 25
3.3 模糊邏輯控制器 26
3.3.1 模糊化界面 28
3.3.2 知識庫 29
3.3.3 模糊推論機構 31
3.3.4 解模糊化界面 32

第四章 模糊控制器設計與系統模擬 34
4.1 倒單擺系統模型之建立 34
4.2 SIRMs模糊控制器的設計 38
4.2.1 設定SIRMs 40
4.2.2 設定動態重要度數的動態變數 41
4.2.3 設定動態重要度數的控制參數 42
4.2.4 SIRMs模糊控制器的方塊圖 43
4.3 利用基因演算法設計SIRMs模糊控制器 44
4.4 利用基因演算法設計之SIRMs模糊控制器之模擬結果 47
4.5 模擬結果討論 57


第五章 結論與建議 59
5.1 結論 59
5.2 建議 60

參考文獻 61

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