臺灣博碩士論文加值系統

在設計一個模糊系統時，將會面對的挑戰之一是如何在計算效率和系統表現之間作一折衷的判斷。基本上，規則數目越多，模糊系統的效能也會越好；然而，所需付出的代價卻是計算量會變得極為龐大。在本論文中，我們提出一個簡單且有效的方法來解決這個困境。本論文利用了Kohonen的自我組織特徵映射圖演算法則(Self-organizing feature map, 簡稱SOM) 本身所具備的向量量化與拓撲關係的性質，來為模糊建模提供一個有效的方法。透過特徵映射圖的向量量化特性，我們可以搜尋出最具代表性的群聚中心；然後再運用其拓樸保存的特性以選擇出一組最具影響力的規則，而這組規則將被使用在系統輸出的計算。透過以上所提的方式，此系統在計算效率和系統表現之間能提供使用者一個適當的解決辦法。此外，為了進一步增加系統運算的速度，我們提出了一個迅速找尋得勝者的方法，讓我們可以從特徵映射圖中快速地將得勝者找出。為了驗證此以自我組織特徵映射圖為基礎之類神經模糊系統的效能，我們分別應用此系統於圖樣識別和系統鑑別的問題上。透過與傳統的類神經模糊系統的比較，我們的系統無論在辨識率或學習速度上都較傳統系統為佳。

One of the challenges that arise in designing a fuzzy system is the trade-off between computational efficiency and performance. Basically, the more rules, the more powerful the fuzzy system becomes. However, the price paid for the high performance is that the computational load becomes extremely large. In this thesis, the author proposes an appealing and easy solution to solve the dilemma. This thesis presents an efficient scheme for fuzzy modeling by using the Kohonen’s self-organizing feature map (SOM) algorithm through its vector quantization feature and its topological property. The vector quantization feature of feature maps is used to search a good supply of most representative cluster centers. Then the topology-preserving feature is fully utilized to select a set of most influential rules to be used in the computation of system outputs. By behaving this way, the proposed SOM-based fuzzy system provides an appealing solution to the trade-off between computational efficiency and performance. Besides, in order to further accelerate the computation of system outputs, the author also proposes a fast winner finding method to quickly locate winners in a feature map. To demonstrate the effectiveness of the proposed SOM-based fuzzy system, the proposed SOM-based fuzzy systems is applied on the problems of pattern recognition and system identification. From the simulation results show that the proposed SOM-based fuzzy system outperforms the conventional one-dimensional structured neuro-fuzzy systems on the recognition rates and the learning speed.

中文摘要 …………………………………………………………I
英文摘要 …………………………………………………………II
目錄 ………………………………………………………………IV
圖目錄 ……………………………………………………………VI
表目錄 ……………………………………………………………VIII
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
第一章緒論 ……………………………………………………1
1.1研究背景 ………………………………………………1
1.2類神經網路簡介 ………………………………………4
1.3模糊系統簡介 …………………………………………6
1.4論文架構 ………………………………………………8
第二章以放射狀基底函數為基礎之模糊系統 ………………9
2.1k-means演算法則 …………………………………… 9
2.2放射狀基底函數網路 ……………………………… 12
2.3系統架構 …………………………………………… 14
2.3.1網路初始化 ………………………………………… 15
2.3.2隱藏層的訓練法 …………………………16
2.3.3輸出層的訓練法 …………………………16
第三章以自我組織特徵映射圖為基礎之模糊系統 ……… 17
3.1自我組織特徵映射圖網路 ………………………… 17
3.1.1自我組織特徵映射圖演算法則 …………17
3.1.2參數的選擇 ………………………………20
3.1.3細調自我組織特徵映射圖 ………………24
3.2系統架構 …………………………………………… 25
3.2.1網路初始化 ………………………………………… 27
3.2.2模仿式學習法 ……………………………30
3.2.3採掘式學習法 ……………………………31
3.3快速搜尋得勝者的方法 …………………………… 32
第四章模擬結果 …………………………………………… 37
4.1550筆二維資料集合的分類問題 ……………………37
4.2系統鑑別的問題 …………………………………… 40
4.3時間序列預測的問題 ……………………………… 42
4.4電壓安全餘度估測的問題 ………………………… 44
第五章結論與展望 ………………………………………… 49
第六章參考文獻 …………………………………………… 52

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