小腦模型演算控制器一種類似類神經網路的智慧型控制器。不同於類神經網路，小腦模型演算控制器使用查表的方式更新權重，因此有較快的學習速度。本論文以小腦模型演算控制器為主題，分別討論兩個主題。第一個主題，透過三種不同類型的模擬討論模糊小腦模型演算控制器及模糊類神經網路的學習效能。第二主題是將小腦模型演算控制器應用在非線性適應控制上，傳統的非直接適應性非線性控制中，未知受控體的參數估測的準確度占很重要的因素，本論文提出在穩定度分析中考慮參數的估測誤差，並與傳統的非直接適應性非線性控制做比較。在兩個主題的的實驗結果中，證實了小腦模型演算控制器有較快的收斂速度及較好的抗雜訊能力，且本文所提出的適應性非線性控制架構較傳統的架構有更好的誤差收歛及準確的參數估測。

Abstract
CMAC is an intelligent controller like as Neural Networks. Different form Neural Networks, CMAC has been regarded as a type of “table-look-up” method to update the weights. Therefore CMAC can have fast learning speed. There are two themes based on CMAC studied in this thesis. First one is the learning performance comparison between FCMAC and Neural-Fuzzy through three simulation conditions. The second one is that CMAC will be applied to nonlinear adaptive control systems. In the original indirect adaptive control, the accuracy of estimated parameter of unknown plant is an important issue, but not being taken care of. In this thesis, an approach, which considers the estimated error in the stability analysis, is proposed. From simulation, it is evident that the CMAC model has faster error convergent speed and better noise tolerance. Furthermore the proposed approach has less convergent error and more accuracy estimated parameter than the original approach does.

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