Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2757
標題: 輻射基底函數式類神經模糊網路
RBF Based Neural Fuzzy Network
作者: 黃朱瑜
Huang, Ju-Yi
關鍵字: RBF;輻射基底函數類神經網路;neural-fuzzy;motor position control;on-line training;CMP;Taguchi method;parameters optimal;類神經模糊網路;交流伺服馬達定位控制;線上學習;化學機械研磨;田口法;參數最佳化
出版社: 機械工程學系
摘要: 
中文摘要
在本論文中,我們結合輻射基底函數類神經網路與模糊理論,將類神經模糊網路由傳統之六層簡化為含輸入層、前件部語言項隸屬函數之模糊化、規則庫與輸出層之四層架構。此新的理論架構在輸入變數僅有一個時,為輻射基底函數類神經網路之一種,在多輸入的時候則是類神經模糊網路。電腦模擬顯示,此新的類神經模糊架構可在極短時間內即完成學習,且在非線性函數之對應與分類上有良好成效。
在實際應用上可分兩部份,
第一部份,我們以所發展之新網路架構與啟發式學習法則結合應用於控制上,實驗結果亦證明,結合輻射基底函數式類神經模糊網路與啟發式學習法則之輻射基底函數式類神經模糊控制系統,在交流伺服馬達之精密定位應用上有極佳之成效。
第二部份,我們結合田口實驗法與輻射基底函數式類神經模糊網路,提出一套系統化之參數設計法則,用以完成化學機械拋光製程之最佳化。實驗結果亦證明,輻射基底函數式類神經模糊網路模式之估測值與實際實驗值皆相當接近,而以輻射基底函數式類神經模糊網路為工具所求得之最佳參數亦可完成相對應之最佳製程,並且經由本研究所提出之二階段最佳化之研磨方式,能夠在製程時間較原來減少1/6的情況下,得到更好之研磨效果。

Abstract
In this thesis, a new neural fuzzy configuration that combines the RBF neural network structure and fuzzy logic theory is proposed.
In this new neural fuzzy structure, the conventional six layers neural fuzzy network is simplified to a four layers neural fuzzy network. For single input problem, this new network structure is a kind of RBF neural network. When a multi-inputs problem is applied, it functions similar a conventional neural fuzzy network.
Computer simulation results show that the proposed new neural fuzzy scheme can be successfully applied to the nonlinear function approximation and classification problems.
To fulfill the on-line training requirement, an efficient heuristic learning rule is included. Experimental results show that the proposed approach can be successfully applied to the precise regulating and tracking problems of an AC servo motor system.
For real industrial application, a systematic approach to achieve global optimal CMP process is carried out. In this new approach, orthogonal array technique in the Taguchi method is adopted for efficient experiment design. The RBFNF neural-fuzzy is then used to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis technique used in the conventional Taguchi method is also implemented to find the local optimal process parameters. Successively, the global optimal parameters are acquired in terms of the trained RBFNF network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results show that the two-stage strategy can perform better then the original approach even though the process time is reduced by 1/6.
URI: http://hdl.handle.net/11455/2757
Appears in Collections:機械工程學系所

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