本論文提出了一種人工DNA蜂后基因演算法（DNA+ QBGA）學習模糊控制器的增益，控制結構，歸屬函數和規則。蜂后輔助遺傳算法（QBGA）的演化過程擁有簡單，快速的程序去找出最佳參數，並採用DNA計算來決定模糊控制器的結構。每一個模糊控制的結構可以被定義成不同的蜂箱，其中包含的控制結構和增益的維度。本篇所提出的人工DNA蜂后基因演算法可以讓歸屬函數和規則庫，在不同的控制結構之間彼此溝通。此外，本文提出一種新型的三步驟的交配法，可以進行不同的奇數維度的歸屬函數之間的交配。第一步驟是父母代（女王蜂和雄蜂）和子代（幼蟲）的維度，透過他們的最小公倍數來擴展到相同的維度。第二步驟是在相對應的空間中隨機選擇父母代的基因。第三步驟子代基因是由他們的父母之間的實數編碼交叉計算取得。最後，將人工DNA蜂后基因演算法運用到模糊控制器之設計，並以倒單擺車和混沌系統之模擬結果展現所提模糊控制器之可行性和有效性。

This thesis proposes an artificial DNA assisted queen bee genetic algorithm (DNA+QBGA) to learn the gains, control structures, membership functions, and rules of the fuzzy controller. The queen bee genetic algorithm (QBGA) possesses simple and fast evolution process to figure out the best parameters and the DNA computing is adopted to determine the structure of fuzzy controller. Each fuzzy control structure can be defined by a different bee hive, which contains the control structure and dimension of the gain. The presented DNA+QBGA can make the membership functions and rules communicate with one another among different control structures. Moreover, a novel three-step crossover operation is investigated such that the crossover between different odd dimensions of membership functions can be made. Step one is that the dimensions of parents (queen and drone) and the offspring (brood) are expanded to the same dimension resolved by their least common multiple. Step two is to randomly select the genes from the parents in the corresponding space. Step three is that the offspring gene is calculated by the real-coded crossover between their parents. Finally, the simulation results of the fuzzy controlled cart-pole and chaotic systems demonstrate the feasibility and effectiveness of the proposed schemes.

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