臺灣博碩士論文加值系統

自我組織映射架構的類神經網路是資料分群中常被用使用的一種方法。本論文提出了一種新的改良式自我組織映射演算法。它的神經元鄰近關係不像一般自我組織映射定義在二維的拓撲平面上，而是建立在以立方體呈現的三維拓撲空間。為了避免網路中死點的產生，在其競爭學習的過程中，其網路結構也會隨時間的不同去動態調整每個神經元之間的鍵結情況。最後再結合自我建立模組使其網路結構在競爭學習的過程中能夠自動調整網路大小，而更適應性的處理各種特殊的資料分佈形態。

Self-organizing neural network is one of the methods frequently used in data clustering. In this thesis, we present a new method to improve the self-organizing map algorithm. Instead of the 2-D neighborhood topology in the conventional self-organizing map, a 3-D 6-neighbor topology is adopted in our approach. To avoid the dead (non-functional) neurons and to represent the training data more effectively, the number of neurons and the links between the neurons will be adjusted automatically during the process of the competitive learning by using a self-constructing model.

第一章　序論 2
1.1 研究背景 2
1.3 研究目標及步驟 3
1.4 論文架構 3
第二章　類神經網路 5
2.1 簡介 5
2-2 類神經元的模型及架構 5
2.2.1 網路架構 6
2.2.2 類神經網路學習過程 8
2.3 競爭式學習演算法則 9
2.3.1 競爭式學習法則之學習步驟 9
2.4 自我組織映射網路 (Self-Organizing Map, SOM) 11
2.4.1 側向聯結 11
2.4.2 自我組織映射網路之架構 13
2.4.3 自我組織映射演算法 14
第三章　分群方法概述 15
3.1 簡介 15
3.2 K-means分群法 15
3-3 FCM演算法 16
3.4 自我組織特徵映射改良演算法 17
3.5 BCL演算法 19
3.6 Fuzzy SOM演算法 20
第四章 適應性自我組織映射法 22
4.1 前言 22
4.2 網路基本架構之改變 22
4.3 訓練中網路結構改變方法 23
4.4 自我建立模組之添加 26
第五章　實驗結果 29
5.1 簡介 29
5.2 實驗數據 30
5.3 人工資料數據之比較結果 30
5.3.1 數據1 30
5.3.2 數據2 31
5.3.3 數據3 32
5.3.4 數據4 33
5.3.5 數據5 34
5.3.6 數據6 35
5.4 實際彩色圖片之Luv資料分佈數據比較結果 36
5.4.1 Lenna圖 36
5.4.2 Baboon圖 38
5.5 實驗結果總覽 39
5.6 適應性自我組織映射在特殊資料分佈的應用 42
5.7 適應性自我組織映射在顏色量化上之應用 43
第六章　結論 46
參考書目 47

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