跳到主要內容

臺灣博碩士論文加值系統

(3.231.230.177) 您好!臺灣時間:2021/07/28 22:54
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:許琇雯
研究生(外文):Hsiu-Wen Hsu
論文名稱:應用資料探勘技術於自相關製程中即時偵測管制圖異常形狀之研究
論文名稱(外文):Real-time Pattern Recognition of Control Charts Patterns in Autocorrelated Process by a Data Mining Based Approach
指導教授:顧瑞祥顧瑞祥引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
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:2009
畢業學年度:97
語文別:中文
論文頁數:111
中文關鍵詞:統計製程管制圖形辨識決策樹學習理論自相關製程
外文關鍵詞:Statistical Process ControlPattern RecognitionDecision Tree LearningAutocorrelated Processes.
相關次數:
  • 被引用被引用:1
  • 點閱點閱:571
  • 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:3
工業界將統計製程管制(statistical process control, SPC) 方法廣泛的應用於製程監控的領域,主要的目的是在監控製程是否存在可歸屬原因 (assignable causes) 所產生的變異,以便提早採取製程改善之行動,避免增加產品額外的生產成本。而在統計製程管制方法中,管制圖 (control chart) 是最常被應用之重要工具,用來決定系統狀態並偵測製程中可能隨時發生的異常情況,這是屬於一種分類問題。管制圖上出現異常的形狀 (pattern) 常是造成製程失控的特定原因,所以辨識與分析管制圖形狀 (control chart pattern, CCP) 就成了一個重要課題。近年來資料採礦技術的其中一個技術決策樹 (decision tree, DT) 被廣泛的應用在分類問題上,並且有許多研究指出決策樹具有良好的效益,因此本研究擬應用決策樹來做為線上偵測製程之監控系統。傳統上,統計製程管制圖是在製程數據滿足常態分配且獨立性的假設下發展。然而,在實際的工業製程中已採用自動化之生產及檢驗方式,導致製程數據間具有高度之自我相關性,在這種情況下傳統之製程管制法並不適用,將造成錯誤警報增加。因此,本研究所探討的製程是模擬在自我相關製程環境中。本研究主要目的在於將決策樹學習應用於即時辨識自相關製程(autocorrelated processes)中的CCP,並且探討其可行性。模擬的結果顯示以DT為基礎之系統模式的快速學習特性讓使用者可以線上監控製程,且在線上即時情況下能學習新的知識 (knowledge),此特性讓一個CCP辨識系統在面對一個動態製造環境時,更具彈性。
Statistical process control (SPC) is an important method for control process in industry. It can detect assignable cause during the process control which may occur and provide help to improve process and reduce unnecessary product cost. Hence, control chart is an important tool at statistical process control. Control charts can detect abnormal status during the process control which may occur at any time. Essentially, the judgement of the process states can be seen as a classification problem in artificial intelligence. Effectively recognizing control chart patterns (CCPs) is a critical issue in statistical process control, since unnatural CCPs indicate potential quality problems at an early stage, to avoid defects before they are produced. Recently, decision tree (DT) is generally used in classification pattern, and a lot of researches point out that DT have excellent performances. This study examines the feasibility of utilizing a data mining technique DT learning in on-line CCP recognition for process with various levels of autocorrelation. An empirical comparison using simulation indicates that the fast learning of the DT model gives the SPC user the potential for building an automated CCP recognition system that can not only be applied on-line but also be trained in real time. This feature could make the CCP recognition system more adaptable to a dynamic manufacturing scenario.
中文摘要 --------------------------------------------- i
英文摘要 --------------------------------------------- ii
誌謝-------------------------------------------------- iii
表目錄------------------------------------------------ vi
圖目錄 --------------------------------------------- viii
符號說明 --------------------------------------------- ix
第一章 緒論----------------------------------------- 1
1.1. 研究背景與動--------------------------------- 1
1.2. 研究目的------------------------------------- 3
1.3. 研究範圍------------------------------------- 4
1.4. 研究假設------------------------------------- 6
1.5. 研究方法與流程------------------------------- 7
1.6. 論文架構------------------------------------- 10
第二章 文獻探討------------------------------------- 12
2.1. 統計製程管制圖之分析------------------------- 12
2.2. 非隨機性辨識之相關文獻----------------------- 16
2.3. 應用類神經網路於製程非隨機性辨識相關文獻----- 17
2.4. 自我相關之統計製程管制法相關文獻------------- 21
2.5. 應用決策樹理論相關文獻----------------------- 22
2.6. 特徵擷取相關文獻----------------------------- 25
2.7. 蒙地卡羅模擬方法應用之相關文獻--------------- 27
第三章 決策樹--------------------------------------- 29
3.1. 資料探勘------------------------------------- 29
3.2. 決策樹之簡介--------------------------------- 31
3.2.1. 決策樹演算法之進行步驟----------------------- 32
3.2.2. C4.5演算法----------------------------------- 33
3.3. 決策樹之基本原理與構成要素------------------- 35
3.4. 決策樹之優點--------------------------------- 37
3.5. 決策樹之應用--------------------------------- 39
第四章 自相關製程----------------------------------- 40
4.1. 時間序列模式--------------------------------- 40
4.2. 自迴歸模式----------------------------------- 41
4.3. 自我相關性製程的監控------------------------- 41
第五章 管制圖非隨機性形狀--------------------------- 43
5.1. 管制圖非隨機性形狀之種類--------------------- 43
5.2. 製程數據蒐集--------------------------------- 48
5.3. 蒙地卡羅模擬法之基本原理與架構--------------- 49
5.3.1. 模擬的定義與方法----------------------------- 49
5.3.2. 蒙地卡羅模擬法------------------------------- 51
5.4. 自相關環境下之製程數據----------------------- 53
5.5. 管制圖非隨機性形狀之數學式------------------- 54
5.6. 製程中非隨機性形狀辨識程序所具備條件--------- 56
第六章 製程統計特徵值------------------------------- 57
6.1. 平均值--------------------------------------- 57
6.2. 標準差--------------------------------------- 58
6.3. 偏態係數------------------------------------- 58
6.4. 峰態係數------------------------------------- 58
6.5. 斜率----------------------------------------- 59
6.6. 皮爾森相關係數------------------------------- 59
第七章 以決策樹為基礎的管制圖之異常辨識系統--------- 60
7.1. 系統架構------------------------------------- 60
7.2. 分析視窗------------------------------------- 62
7.3. CCP範例的產生-------------------------------- 63
7.4. DT學習--------------------------------------- 65
7.5. 衡量指標------------------------------------- 67
第八章 辨識系統績效評估----------------------------- 68
8.1. 評估方法與評估指標--------------------------- 68
8.2 評估程序------------------------------------- 69
8.3. 動態測試與相關參數設定----------------------- 70
8.3.1. 決策樹之動態測試----------------------------- 71
8.3.2. 自相關環境下動態測試相關參數設定------------- 73
第九章 實驗結果與分析------------------------------- 74
第十章 結論與貢獻----------------------------------- 99
10.1. 結論----------------------------------------- 99
10.2. 貢獻----------------------------------------- 100
參考文獻 --------------------------------------------- 101
1.丁勝興 (2003),資料探勘在用藥輔助決策系統之研究,元智大學資訊管理研究所,碩士論文。
2.王仁達 (1988),應用類神經網路偵測製程平均值變化:設計策略之研究,元智大學,碩士論文。
3.吳育儒 (1998),決策樹中移除不相關值問題的研究,淡江大學資訊工程研究所,碩士論文。
4.吳宗燦 (1997),決策樹漸進學習法中熵數變化之估測研究,中原大學資訊工程研究所,碩士論文。
5.吳柏林 (1995),時間數列分析導論,華泰書局。
6.吳盈宜 (1999),歸納學習法中決策樹連續屬性分割點之選擇,成功大學資訊管理研究所,碩士論文。
7.吳國禎、趙一平、蘇振隆 (2002),「資料探索方法在醫學資料庫之評估」,中原學報,30卷,1期,頁51-61,中原大學出版。
8.吳聰宏 (1994),類神經網路應用在品質管制中相關性製程數據之管制,元智大學工業工程研究所,碩士論文。
9.李紹綸 (1998),知識發掘在信用卡之應用,淡江大學資訊工程研究所,碩士論文。
10.官亭汝 (1997),應用類神經偵測製程平均值及變異數變化之研究,元智大學工業工程研究所,碩士論文。
11.林宏晉 (2003),不確定因素考量下之都市鄰里公園區位選擇研究,朝陽科技大學建築及都市設計研究所,碩士論文。
12.林昕怡 (2000),技術移轉決策模式之構建-應用歸納決策樹,成功大學工業管理研究所,碩士論文。
13.林茂文 (1992),時間數列分析與預測,華泰書局。
14.林聖義 (2003),資料採礦技術應用於艦艇維修備料件預測之研究,國防大學中正理工學院兵器系統工程研究所,碩士論文。
15.林裕章 (1992),類神經網路應用於統計製程管制分隨機性模型之研判,元智大學工業工程研究所,碩士論文。
16.林榮和 (1999),應用類神經網路於管製圖非隨機性模型之辨認,元智大學工業工程研究所,碩士論文。
17.邱美珍 (1995),決策樹學習法中連續屬性之分類研究,中原大學資訊工程研究所,碩士論文。
18.邱淳奕 (1993),國語詞彙辨識決策樹之自動設計,中興大學應用數學研究所,碩士論文。
19.施炳光 (2007),結合製程統計特徵值與類神經網路於管制圖異常形狀之辨識,國立虎尾科技大學工業工程與管理研究所,碩士論文。
20.孫世偉 (2000),模糊邏輯在決策樹上之應用,交通大學資訊科學研究所,碩士論文。
21.張衡閣 (2002),一個資料庫多維度序列法則探勘方法,朝陽科技大學資訊管理系,碩士論文。
22.許芳勳 (2000),動態可靠度模型之探討及其應用,國立中央大學機械工程研究所,博士論文。
23.許益鑫 (1998),教會醫院個人捐款者特性之研究:CART 法之應用,陽明大學醫務管理研究所,碩士論文。
24.連惟謙 (2003),應用資料分析技術進行顧客流失與顧客價值之研究,中原大學資訊管理研究所,碩士論文。
25.陳信嘉 (1999),管制圖非隨機樣式之辨識與參數之估計,元智大學工業工程研究所,碩士論文。
26.陳建至 (1997),以類神經網路為基礎監視計數值品質特性,元智大學工學院,碩士論文。
27.陳重銘 (1994),結合直線最適法於決策樹修剪之影響研究,中山大學資訊管理研究所,碩士論文
28.陳偉 (1999),決策樹中不相關的條件值問題之探討,淡江大學資訊工程研究所,碩士論文。
29.陳欽賢 (1992),財務危機預警專家系統,淡江大學管理科學研究所,碩士論文。
30.陳雙卯 (2003),海外指數連動債卷之設計、評價與避險分析,國立中山大學財務管理學系研究所,碩士論文
31.陳顯旭 (2000),以基因規劃法建構迴歸樹,元智大學資訊管理研究所,碩士論文。
32.傅麗容 (1993),以決策樹方法預測匯率變動趨勢,成功大學工業管理研究所,碩士論文。
33.彭文正 (2001),資料採礦-顧客關係管理暨電子行銷之應用,數博網資訊股份有限公司,台北。
34.曾尹玢 (2003),以相關性蒙地卡羅模擬進行二維不確定性與變異性分析:應用於鮭魚存活率模式,國立臺灣大學生物環境系統工程學系暨研究所,碩士論文。
35.曾慶安 (1994),類神經網路在品質管制上之應用:以倒傳遞網路偵測製程個別數據之平均值及變異數的變化,元智大學工業工程研究所,碩士論文。
36.童冠燁、潘正坤 (2002),「以決策樹獲取製造系統排程知識之研究」,中華管理學報,3卷,1期,頁21-40,中華大學管理學院出版。
37.黃天佑 (1993),模糊熵決策樹歸納學習法,中山大學資訊管理研究所,碩士論文。
38.黃金安 (2000),健保門診診篩檢高血壓之成本效果分析-以預防腦中風為例,中國醫藥學院醫務管理研究所,碩士論文。
39.黃信智 (2003),灰色系統理論與系統動力學之應用-以污水處理廠為例,朝陽科技大學環境工程與管理系,碩士論文。
40.黃雅芳 (2003),應用資料採礦技術於資料庫加值中的插補方法比較,國立政治大學統計研究所,碩士論文。
41.黃聖傑 (1994),樹狀迴歸的方法與其應用在調查資料之分析,成功大學統計學研究所,碩士論文。
42.楊其龍 (1997),應用類神經網路於相關性數據之製程管制法研究,大葉工學院,碩士論文。
43.楊超然 (1990),作業研究,七版,三民書局發行,台北。
44.葉小蓁 (1996),時間序列分析與應用,台北。
45.廖香娟 (2000),強健性發音表示集及狀態分享式決策樹之產生,成功大學資訊工程研究所,碩士論文。
46.熊家誠 (2003),自動化螢光顯微影像之次細胞結構辨識,中原大學醫學工程研究所,碩士論文。
47.管中閔 (2000),統計學關觀念與方法,華泰文化事業股份有限公司,台北。
48.劉德新 (1988),以決策樹歸納方法為基礎的知識獲得系統,成功大學工業管理研究所,碩士論文。
49.蔡政良 (1996),以特徵為基之管制圖非隨機性模型的辨認-使用類神經網路,元智工學院工業工程研究所,碩士論文。
50.鄭春生 (1989),品質管理,二版,育友,台北。
51.鄭春生、鄭靜彥 (1999),「以類神經網路辨識製程個別值數據之平均值、變異數及相關性之變化」,品質學報,六卷,頁29- 43。
52.蕭文峰 (1994),運用決策樹歸納學習法預測連續數值,中山大學資訊管理研究所,碩士論文。
53.蕭豐達 (2003),應用模糊理論與決策樹法於空載影像建築物分類之探討,朝陽科技大學環境工程與管理系,碩士論文。
54.謝昆霖 (1994),類神經網路在品質管制上之應用:非隨機性變化之偵測,元智工學院工業工程研究所,碩士論文。
55.謝國義 (1998),決策樹形成過程中計算複雜度之改善研究,成功大學工業管理研究所,碩士論文。
56.簡禎富、李培瑞、彭誠湧 (2003),「半導體製程資料特徵萃取與資料挖礦之研究」,10卷,1期,頁63-84,國立中山大學管理學院出版。
57.簡靜慈 (2000),使用基因演算法建構決策樹,元智大學資訊管理研究所,碩士論文。
58.蘇仁德 (1998),應用類神經網路偵測製程平均值及變異數變化:設計策略之研究,元智大學工業工程研究所,碩士論文。
59.鐘大歡 (2003),鑑別闊葉樹材專家系統建立之研究,國立中興大學森林學系,碩士論文。
60.顧瑞祥 (2003),「應用基因演算法最佳化偵測製程異常之類神經網路」,中華民國品質學會,第39屆年會暨第九屆全國品質研討會。
61.顧瑞祥和薛友仁 (2004),「應用動態學習之神經網路偵測與分析管制圖之異常形狀」,中華民國品質學會第40 屆年會 (高雄市分會第30 屆年會) 暨第十屆全國品質管理研討會論文集,A1-7,頁66- 76。
62.Albanis G.T. and R.A. Batchelor (1999). “Five Classification Algorithms to Predict HighPerformance Stocks”, 6th International Conference on Forecasting Financial Markets.
63.Alex Burdorf and Paul Swuste (1999). “An Expert System for the Evaluation of Historical Asbestos Exposure as Diagnostic Criterion in Asbestos-related Diseases”, British Occupational Hygiene Society, vol. 43, no. 1, pp. 57-66.
64.Alwan, L.C. (1992). Eects of Autocorrelation on Control Chart Performance, Communication in Statistics: Theory and Methods.
65.Alwan, L.C. and Roberis, H.V. (1988). “Time-Series Modeling for Statistical Process Control”, Journal of Business and Econonric Statistics, vol. 6, pp. 87-95.
66.Apte, C., R. Bibelnieks, E. Natarajan, E. Pednault, F. Tipu, D. Campbell and B. Nelson (2001). “Segmentation-based Modeling for Advanced Target Marketing”, proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discorvery and Data Mining, pp. 408-413.
67.Beneke, M., Leemis, L.M., Schlegel, R.E., & Foote, B.L. (1988). “Spectral analysis in quality control: a control chart based on the peridogram”, Technomertrics, vol. 30, pp. 63-70.
68.Box, G.E.P., Jenkins, G.M. and MacGregor, J.F. (1974). “Some recent advances in forecasting and control”, Part II. J. Royal Stat. Soc., vol. 23, pp. 158-179.
69.Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994). Time Series Analysis Forecasting and Control, Printice Hall, New Jersey.
70.Brery, M.J.A. and Linoff, G. (1997). Data Mining Technique for Marketing, Sale, and Customer Support, Wiley Computer, New York.
71.Cabena, P., Hadinaian, P.O., Standler, D.R.J., Verhees, J. and Zannasi, A. (1998). Discovering Data Mining form Concept to Implementation, Prentice Hall.
72.Champ, C.W. and Woodall, W.H. (1987). “Exact Results for Shewhart Control Chart with Supplementary Runs Rules”, Technometris, vol. 29, no. 4, pp. 393-399.
73.Chan, P.K., Fan, W., Prodromidis, A.L. and Stolfo, S.J. (1999). “Distributed Data Mining in Credit card Fraud Detection”, IEEE Intelligent Systems, vol. 14, pp. 67-74.
74.Chang, S.I. and Aw, C.A. (1996). “A neural fuzzy control chart for detecting and classifying process mean shift”, International Journal of Production Research, vol. 34, no. 8, pp. 2265-2278.
75.Chen, M.S., Han, J. and Yu, P.S. (1996). “Data Mining: An Overview from a Database Perspective”, IEEE Transactions on Knowledge and Data Engineering, vol. 8, pp. 883-886.
76.Cheng, C.S. and Hubele, N. (1989). “A framework for a rule-based deviation recognition system in statistical process control”, International Industrial Engineering Conference, pp. 677-682.
77.Cheng, C.S. and Tzeng, C.A. (1994). “A neural network approach for detecting shifts in process mean and variability”, Journal of the Chinese Institute of Industrial Engineers, vol. 11, no. 2, pp. 67-75.
78.Cheng, C.S. (1989), Group technology and expert systems concepts applied to statistical process control in small-batch manufacturing, Arizona State University, Tempe, AZ, Ph. D. Dissertation.
79.Cheng, C.S. (1995), “A multi-layer neural network model for detecting changes in the process mean”, Computers & Industrial Engineering, vol. 28, no. 1, pp. 51-61.
80.Cheng, C.S. (1997), “A neural network approach for the analysis of control chart patterns”, International Journal of Production Research, vol. 35, no 3, pp. 667-697.
81.Chiu, C.C., Chen, M.K. and Lee, K.M (2001). “Shift recognition in correlated process data using a neural network”, International Journal of Systems Science, vol. 32, pp. 137-143.
82.Cook, D.F. and Chiu, C.C. (1998). “Using radial basis function neural networks to recognize shift in correlated manufacturing process parameters”, IIE Transaction, vol. 30, pp. 227-234.
83.Davis, R.B. and Woodall, W.H. (1988). “Performance of the Control Chart Trend Rule under Line Shirt”, Journal of Quality Technology, vol. 20, no. 4, pp. 105-115.
84.Duncan, A.J. (1986). Quality Control and Industrial Statistics, 5th ed., Irwin Book Company, Illinois.
85.Fayyad, U., Piatetsky Shapiro, G. and Smyth, P. (1996). “The KDD Process for Extracting Useful Knowledge form Volumes of Data”, Communications of the ACM, vol. 39, pp. 27-34.
86.Fayyad, U.M. (1996). “Data Mining and knowledge Discovery: MakingSense Out of data”, IEEE Expert, vol. 11, Issue 5, pp. 20-25.
87.Grant, E.E. and Leavenworth, R.S. (1988), Statistical Quality Control, 6th ed., McGraw-Hill, New York.
88.Grupe, F.H. and Owrang, M.M. (1995). “Database Mining Discovering New Knowledge and Cooperative Advantage”, Information Systems Management, vol. 12, pp. 26-31.
89.Guh, R.S. (2005). “A hybrid learning-based model for on-line detection and analysis of control chart patterns”, Computer & Industrial Engineering, vol. 49, pp. 35-62.
90.Guh, R.S. (2002). “Robustness of the neural network based control chart pattern recognition system to non-normality”, International Journal of Quality and Reliability Management, vol. 19, no. 1, pp. 97-112.
91.Guh, R.S. (2003), “Integrating artificial intelligence into on-line statistical process control”, Quality and Reliability Engineering International, vol. 19, no. 1, pp. 1-20.
92.Guh, R.S. and Tannock, J.D.T. (1999). “Recognition of control chart concurrent patterns using a neural network approach”, International Journal of Production Research, vol. 37, no. 8, pp. 1743-1765.
93.Guo, Y. and Dooley, K.J. (1995). “Distinguishing between mean, variance and autocorrelation changes in statistical quality control”, International Journal of Production Research, vol. 33, pp. 497-510.
94.Guo, Y. and Dooley, K.J. (1992). “Identification of change structure in statistical process control”, International Journal of Production Research, vol. 30, no. 7, pp. 1655-1669.
95.Ham, F.M. and Kostanic, I. (2001). Principles of Neurocomputing for Science & Engineering, McGraw-Hill, New York.
96.Harris, T.J. and Ross, W.H. (1991). “Statistial Process Control Procedured for Correlated Observations”, The Canadian Journal of Chemical Engineering, vol. 69, pp. 48-57.
97.Hassan, A. (2003). “Improved SPC chart pattern recognition using statistical features”, International Journal of Production Research, vol. 41, no. 7, pp. 1587-1603.
98.Hastie, T., J. Friedman and R. Tibshirani (2001). The element of statistical learning, Springer-Verlag, New York.
99.Hwarng, H.B. (2004). “Detecting process mean shift in the presence of autocorrelation a Neural-network based monitoring scheme”, International Journal of Production Research, vol. 42, pp. 573-595.
100.Hwarng, H.B. and Hubele, N. (1993). “Back-Propagation Recognizers for X-bar Control Chart”, Computer and Industrial Engineering, vol. 24, pp. 219-235.
101.Hwarng, H. and Chong, C. (1994). “Detecting process non-randomness through a fast and cumlative learning ART-based pattern recognizer”, International Journal of Production Research, vol. 33, pp. 1817-1833.
102.Hwarng, H.B. (1995). “Proper and effective training of a pattern recognizer for cyclic data”, IIE Transactions, vol. 27, no. 6, pp. 746-756.
103.Hwarng, H.B. and Hubele, N.F. (1993). “X-bar control chart pattern identification through efficient off-line neural network training”, IIE Transactions, vol. 25, no. 3, pp. 27-40.
104.Hwarng, H.B. (2004). “Detecting process mean shift in the presence of autocorrelation: A neuralnetwork based monitoring scheme”, International Journal of Production Research, vol. 42, pp. 573-595.
105.Hwarng, H.B. (2005). “Simultaneous identification of mean shift and correlation change in AR(1) processes”, International Journal of Production Research, vol. 43, pp. 1761-1783.
106.Kim, Sung-Min, Jong-Dal Kim, Jeong-Hee Hong, Do-Won Nam, Dong-Ha Lee,Jeon-Young Lee (2000). “A System for Association Rule Finding from an Internet Portal Site".
107.Kleissner, C. (1998). “Data mining for the enterprise”, In Proceedings of the Thirty-First Hawaii International Conference on, vol. 7, pp. 295-304.
108.Lavangnananda, K. and Nakkathon, A. (2003). “Improving Features Extraction in Control Chart Patterns”, Proceedings of the 7th National Computer Science and Engineering Conference, Thailand, pp. 364-369.
109.Lavangnananda, K. and Piyatumrong, A. (2005). “Image processing approach to features extraction in classification of control chart patterns”, Proceedings of the 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications, Helsinki, pp. 85-89.
110.Lynd D. and Bacon (2002), “Marketing”, Handbook of Data Mining and Knowledge Discovery, edited by Willi Klösgen & Jan M. Żytkow, pp. 715-725.
111.Maragah, H.D. and Wooddall, W.H. (1992). “The E_ect of Autocorrelation on the Retrospective X-Chart”, Journal of tatistical Computation and Simulation, vol. 40, pp. 29-42.
112.Michael, J.A. and Linoff, G.. (1997), Data Mining Technique: for Marketing, Sales and Customer Support, Wiley Computer Publishing, New York.
113.Montgomery, D.C. and C.M. Mastrangelo. (1991), “Some statistical process control methods for autocorrelated data” , Journal of Quality Technology, vol. 23, no. 3, pp. 179-193.
114.Montgomery, D.C. (1997). Introduction to Statistical Quality Control, New York.
115.Montgomery, D.C. (2001). Introduction to Statistical Quality Control, 4th ed., John Wiley and Sons, New York.
116.Nelson, L.S. (1984). “Interpreting Shewhart X Control Chart”, Journal of Quality Technology, vo1. 17, no. 2, pp. 114-116.
117.Page, E.S. (1954). “Continuous inspection schemes”, Biometrika, vol. 41, pp. 100-115.
118.Pandit, S.M. and Wu, S. (1983). Time Series and System Analysis with Applications, New York.
119.Pandya, A.S. and Macy, R.B. (1996). Pattern Recognition with Neural Network in C ++, CRC, Florida.
120.Pham, D.T. and Oztemel, E. (1992). “Control Chart Pattern Recognition Using Neural Network”, Journal of System Engineering, vol. 2, pp. 256-262.
121.Pham, D.T. and Wani M.A. (1997). “Feature-based control chart pattern recognition”, International Journal of Production Research, vol. 35, pp. 1875-1890.
122.Pham, D.T. and Oztemel, E. (1994), “Control Chart Pattern Recognition Using Learning Vector Quantization Networks”, International Journal of Production Research, vol. 32, no. 3, pp. 721-729
123.Pugh, G.A. (1989). “Synthetic neural network for process control”, Computer and Industrial Engineering, vol. 17, pp. 24-26.
124.Pugh, G.A. (1991). “A comparison of neural networks to SPC chart”, Computer and Industrial Engineering, vol. 21, pp. 253-255.
125.Quinlan, J.R. (1986). “Induction of decision trees”, Mach Learn, vol. 1, no. 1, pp, 81-106.
126.Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, USA.
127.Roberts, S.W. (1959). “Control chart test based on geometric moving averages”, Technometrics, vol. 1, pp. 239-250.
128.Roberts, S.W. (1958). “Properties of control chart zone tests”, Bell System Technical Journal, vol. 37, pp. 83-113.
129.Runger, G.C., Willemain, T.R. and Prabhu, S. (1995). “Average run lengths for CUSUM control charts applied to residuals”, Comm in Stat-Theory and Methods, vol. 24, pp. 273-282.
130.Sahrmann, H (1979). “Set-up assurance through time series analysis”, Journal of Quality Technology, vol. 11, pp. 105-115.
131.Schilling, E.G. and Nelson, P.R. (1976). “The effect of non-normality on the control limits of X-bar chart”, Journal of Quality Technology, vol. 8, no. 4, pp. 183-188.
132.Scholkopf, B. and Smola, A.J. (2000). Statistical learning and kernel methods, Cambridge, USA.
133.Smith, A.E. (1994). “X-bar and R control interpretation using neural computing", International Journal of Production Research, vol. 32, no. 2, pp. 309-320.
134.Spedding, T.A. and Rawlings, P.C. (1994). “Non-normality in statistical process control measurements”, International Journal if Quality and Reliability Management, vol. 11, no. 6, pp. 27-37.
135.Spurrier, J.D., & Thombs, L.A. (1990). “Control Chart for Detecting Cyclical Behavior”, Technomentrics, vol. 32, no. 2, pp. 163-171.
136.Swift, J. (1987). Development of A Knowledge Based Expert System for Control Chart Pattern Recognition and Analysis, Doctoral Thesis, The Oklaoma State University.
137.Tontini, G. (1998). “Robust learning and identification of patterns in statistical process control charts using a hybrid RBF fuzzy artificial neural network”, IEEE International Joint Conference on Neural Network Proceedings, vol. 3, pp. 1694-1699.
138.Utku, H. (2000). “Application of the feature selection method to discriminate digitised wheat varieties", Journal of Food Engineering, vol. 46, pp. 211-216.
139.Wang, T.Y. and Chen, L.H. (2002). “Mean shift detection and classification in multivariate process: a neural-fuzzy approach”, Journal of Intelligent Manufacturing, vol. 13, pp. 211-221.
140.Wardell, D.G., Moskowitz, H. and Plante, R.D. (1994). “Run-length distributions of special-cause control charts for correlated processes”, Technometrics, vol. 36, pp. 3-17.
141.Wei, W.S. (1990). Time Series Analysis Univariate and Multivariate Methods, Addison-Wesley, New York.
142.Western Electric. (1958). Statistical Quality Control Handbook, Western Electric Company, New York.
143.Yourstone, S.A. and Zimmer, W.J. (1992). “Non-normality and the design of control charts for average”, Decision Sciences, vol. 23, pp. 1099-1113.
144.Yourstone, S.A. and Montgomery, D.C. (1989). “A time-Series Approach to Discrete Real-Time Process Quality Control”, Quality and Reliability Engineering International, vol. 5, pp. 309-317.
145.Yourstone, S.A. and Montgomery, D.C. (1991). “Detection of Process Upsets-Sample Autocorrelation Control Chart Applications”, Quality and Reliability Engineering International, vol. 7, pp. 133-140.
146.Zeki, A.A. and Zakaria, M.S. (2000). “New primitive to reduce the effect of noise for handwritten features extraction”, IEEE Intelligent Systems and Technologies for the New Millennium Proceedings, Tencon.
147.Zorriassatine, F. and Tannock, J.D.T. (1998). “A review of neural networks for statistical process control”, Journal of Intelligent Manufacturing, vol. 9, no. 3, pp. 209
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top