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研究生:王志峯
研究生(外文):Chih-Fehn Wang
論文名稱:整合增長式自組織映射圖與遺傳演算法之發展與應用
論文名稱(外文):The Development and Application of Integration of Growing Self-Organizing Map and Genetic Algorithm
指導教授:郭人介郭人介引用關係
口試委員:陳凱瀛駱至中
口試日期:2008-05-23
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:77
中文關鍵詞:自組織映射圖群集分析遺傳演算法增長式自組織映射圖
外文關鍵詞:Self-Organizing MapClustering AnalysisGenetic AlgorithmGrowing Self-Organizing Map
相關次數:
  • 被引用被引用:1
  • 點閱點閱:253
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近幾年來,集群分析(Clustering Analysis)已廣泛被應用於各個層面,如:商業和教育、社會科學、遺傳學、生物科學等,群集其主要目的就是利用群體中具有相同統計特性聚成同一群,使得同群之間的同質性較高,而不同群之間有顯著的差異。因此,本研究利用自組織映射圖(Self-Organizing Map, SOM)發展出一個兩階段群集分析方法。第一階段為增長式自組織映射圖(Growing Self-Organizing Map, GSOM),其將透過輸入資料本身之結構產生適合的網路拓樸,並透過傳統SOM之訓練來產生網路權重向量,而第二階段則再利用連續型遺傳演算法(Continuous Genetic Algorithm, CGA)找出全域最佳解。
為驗證本研究所提之集群分析方法,提出GA、GASOM(Genetic Algorithm Self-Organizing Map)和SOM+GA,首先採用4個已知群集分佈的基準資料集:Iris、Wine、Vowel及Glass來分析何種方法之網路效益為最佳。研究之結果顯示GASOM為最佳,不但可以加速收斂效果而且也可以獲得更佳的解。
此外,本研究亦透過GSOM與GASOM的結合,將其應用在電池評等分級上,結合實際的電池量測結果做一綜合評判,以作為電池自動化分級評等系統之基礎,提供電池相關業者在設計及製造上品質之提升及成本的降低。
In recent years, clustering analysis has been widely applied in may areas, like engineering, management, and bioscience. The purpose is to segment the individuals with the same characteristics in the population into the same group. Thus, those belong to the same group are homogenous and have the same characteristics. On the hand, those belong to different groups are heterogonous and have different characteristics. Therefore, this study attempts to use Growing Self-Organizing Map (GSOM) with integration of Genetic Algorithm (GA) to accelerate searching global solution and reduce the phenomenon of falling into regional solution. The proposed Genetic Algorithm-Based GSOM (GASOM) is consisted of two stages. The first stage will determine the network topology using tradition GSOM while the weights are determined by using genetic algorithm with SOM operator in the second stage.
The proposed method is compared with other two clustering methods using four benchmark data sets, Iris, Wine, Vowel, and Glass. The simulation results indicate that GASOM not only has faster converging speed but also can find the better solution.
In practical application, the proposed method also has been successfully employed to grade Li-ion cells and characterize the quality inspection rules. The results may offer a great assistance to the battery manufacturers and it can provide for improving the quality and decreasing the costs of battery design and manufacturing.
摘要 I
Abstract II
誌謝 IV
目 錄 VI
表目錄 VIII
圖目錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4 研究架構 2
第二章 文獻探討 4
2.1類神經網路(Artificial Neural Network) 4
2.1.1 類神經網路之定義 4
2.1.2 類神經網路之種類 4
2.2自組織映射網路(Self-Organization Maps, SOM) 6
2.2.1 SOM簡介 6
2.2.2 SOM演算法 6
2.2.3 SOM模式改善與分析(楊東昌, 2002) 8
2.2.3.1 資料與群集視覺化 8
2.3.3.2 拓樸保存與次序保存 10
2.3.3.3 網路拓樸之動態網格 12
2.3 增長式自組織映射圖(Growing SOM, GSOM) 13
2.3.1 GSOM簡介 14
2.3.2 GSOM演算法 14
2.3.3 GSOM的優點 16
2.4遺傳演算法(Genetic Algorithm) 16
2.4.1 遺傳演算法之基本運算原理 16
2.4.2 連續型遺傳演算法(continuous genetic algorithm, CGA) 18
第三章 研究方法 20
3.1 研究架構 20
3.2 增長式自組織映射圖(GSOM) 22
3.2.1 GSOM演算法 22
3.3 連續型遺傳演算法(CGA) 28
3.3.1 GCA處理程序 29
第四章 實驗分析 33
4.1 資料集介紹 33
4.2 資料前處理 34
4.3 實驗結果與分析 35
4.3.1 演算法評估準則 36
4.3.2 演算法結果分析 37
4.3.3 演算法收斂情形 38
4.4 演算法參數分析 41
第五章 實證分析 44
5.1 實證資料來源 44
5.2 資料前處理 47
5.3 實證結果與分析 48
5.3.1 GSOM取得拓樸大小 48
5.3.2 分群效果比較 51
5.3.3 實例分群結果 52
5.3.4 分群結果之驗證 54
5.3.5 分群結果的應用探討 57
第六章 結論與建議 59
6.1 研究結論 59
6.1.1 實驗分析結論 59
6.1.2 實證分析結論 59
6.2 研究貢獻 60
6.3 未來研究與建議 60
參考文獻 61
附錄A 各資料集30次實驗結果 64
附錄B 各資料集收斂情形 68
附錄C 敏感度分析30次實驗結果 70
附錄D 實證分析之優勝節點累積圖 73
附錄E 集群結果與量測值對應表 75
附錄F 系統畫面 77
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