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系統識別號 U0026-0107201409153000
論文名稱(中文) 結合小波轉換及支援向量回歸之多變量模糊管制圖
論文名稱(英文) Using Wavelet Transform and Support Vector Regression in Mean Shifts Detection and Classification in Multivariate Process
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 102
學期 2
出版年 103
研究生(中文) 王伯文
研究生(英文) Po-Wen Wang
學號 r36024029
學位類別 碩士
語文別 中文
論文頁數 66頁
口試委員 指導教授-王泰裕
口試委員-施勵行
口試委員-陳梁軒
口試委員-謝中奇
中文關鍵字 最小平方支援向量回歸  小波轉換  多變量模糊管制圖 
英文關鍵字 support vector regression  wavelet transform  multivariate fuzzy neural control chart 
學科別分類
中文摘要 在工業界中,品質管制一直是一個重要的課題,目前管制圖(control chart)為最常被使用的品質管制手法之一。製程從過去的簡單製程轉變為愈趨多元化的製程,因此所使用的管制圖也逐漸從單變量管制圖(univariate control chart)轉變為多變量管制圖(multivariate control chart),其中最常被使用的多變量管制圖為Multivariate EWMA、Multivariate CUSUM和Hotelling’s T^2管制圖,雖然這些管制圖能夠在製程發生偏移時發出警訊,但卻無法直接獲得更多的資訊,通常需透過複雜的計算才能夠獲取如發生偏移的變量資訊,但仍然無法得知發生偏移的幅度大小。
因此有學者提出結合類神經網路的學習能力,以及小波轉換對訊號優異解析能力所建構出的多變量管制圖來監控製程的偏移。在類神經網路中,支援向量回歸基於邊際最小化(margin minimum)理論,找出最佳的預測值,且在預測類別值的效果上比其他回歸方法為佳。本研究利用小波轉換的訊號解析能力與支援向量回歸的預測能力,並結合多變量模糊管制圖,來辨識當製程產生偏移時,造成製程偏移的變量以及其平均值偏移的幅度大小。
本研究所建立的結合小波轉換及支援向量回歸之多變量模糊管制圖,透過實例驗證後,得到在平均值偏移型態之辨識正確率以及在製程偏移下之平均連串長度(out-of-control Average Run Length, ARL )之結果,皆比Fuzzy-BPN方法及Hotelling’s T^2管制圖良好。
英文摘要 In this study, we propose a method to integrate the wavelet transform, multi-output least square support vector regression (MLS-SVR) and multivariate fuzzy control chart for identifying the mean shifts in manufacturing processes. Firstly, the Haar wavelet transform is used to obtain the mean shifts information from the data. Then, these shift information are used as the training examples to obtain the necessary information for the MLS-SVR. Next, the MLS-SVR with trained information is used to predict the output values of the unseen data in the future. Thirdly, the multivariate fuzzy control chart is employed to categorize each output value into the appropriate mean shift category. The proposed method is evaluated by simulation data and the real data from manufacturing processes. The results show that the mean shifts detection of the proposed method has better performance compared to the BPN-Fuzzy control chart (Wang and Chen, 2002) and Hotelling’s control chart (Hotelling, 1947) as far as the correct classification percentage (CCP) and average run length (ARL) concerned.
論文目次 目錄
摘要 ii
英文摘要 iii
謝誌 ix
圖目錄 xii
表目錄 xiii
符號表 xiv
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 1
第三節 研究範圍與假設 2
第四節 研究流程 2
第五節 論文大綱 3
第二章 文獻探討 4
第一節 多變量管制圖 4
第二節 類神經網路 9
第三節 小波轉換 17
第四節 模糊集合理論 19
第五節 類神經網路與小波轉換於管制圖的應用 22
第六節 小結 23
第三章 建立結合小波轉換及支援向量回歸之模糊類神經管制圖 24
第一節 管制圖建立程序 24
第二節 資料前置處理 25
第三節 訓練及測試最小平方支援向量回歸 27
第四節 模糊分類器建構 31
第五節 小結 35
第四章 實例驗證 36
第一節 模擬樣本產生 36
第二節 最小平方支援向量回歸 37
第三節 效能評估 38
第四節 案例分析 44
第五節 小節 50
第五章 結論與建議 52
第一節 結論 52
第二節 建議與未來研究方向 53
參考文獻 54
附錄 59
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