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研究生:林志鴻
研究生(外文):Chih-Hung Lin
論文名稱:應用整體式分類模型於多變量製程變異性異常來源之辨識
論文名稱(外文):Identifying the source of variance shifts in the multivariate process using ensemble classifiers
指導教授:鄭春生鄭春生引用關係
指導教授(外文):Chuen-Sheng Cheng
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
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:2011
畢業學年度:99
語文別:中文
論文頁數:41
中文關鍵詞:多變量管制圖變異性變化整體式學習支援向量機
外文關鍵詞:multivariate control chartvariance shiftensemble learningsupport vector machine
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多變量管制圖可以同時監控多個具有相關性之品質特性,針對多變量製程變異性之監控,常用廣義性變異數 |S| 管制圖進行偵測。多變量管制圖之管制外訊號產生可能是由一個或多個變量所導致,然而當管制圖偵測到製程異常發生時,管制圖卻無法進一步地判別是由多變量製程中哪一個品質特性所造成之變異。為了能夠有效地判斷出發生異常之品質特性為何,在過去研究中提出以人工智慧方法進行異常變量之辨識,且僅使用一個分類模型來辨認異常來源,由於單一分類模型受限於相同的訓練樣本以及固定的參數,其辨識績效有限。本研究以整體式分類模型提升其辨識績效。
本研究將辨識管制外之訊號來源視為一個分類問題,並且提出一個整體式支援向量機辨識系統包含偵測和辨識。當多變量管制圖偵測到異常時,將以辨識系統判斷製程變異性之異常來源。一般常用來建立整體式分類器之多樣性策略為操控訓練樣本,在本研究中提出一個創新之資料多樣性方法,我們依據管制圖之不同統計特性所產生之樣本,來建構整體式分類模型,並以特徵值作為支援向量機分類器之輸入向量來提升辨識系統之績效。本研究以分類之正確率作為評估不同方法之指標,其結果顯示整體式辨識系統有助於提升異常來源辨識之績效。


Multivariate control chart is used in the situation which simultaneous monitoring of two or more related quality characteristics. The generalized variance, |S|, control chart is usually applied to monitor process variability. Out-of-control signals in multivariate control charts may be caused by one or more variables. Although control chart is efficient in detecting a general multivariate shift in the variance, it fails to determine which variables are responsible for the variance shift. Many research papers address these problems and present various artificial intelligence approaches to identify aberrant variables. In the previous studies, only one classifier is applied in recognizing abnormal sources. However, the single model is limited to the same data or parameters setting and none of them could consistently perform well over all datasets.
In this paper, we formulate the interpretation of out-of-control signal as a classification problem. The proposed system includes a SVM ensemble classifiers and a shift detector. When an out-of-control signal is generated, ensemble classifiers will determine which variable is responsible for the variance shift. Manipulating the training sample is usually used to create the diverse models. In the proposed approach, we base on some different statistical properties to construct ensemble and propose using extracted features as predictors to enhance the performance. The performance of the proposed system is evaluated by computing its classification accuracy. Results from studies indicate that the proposed approach is beneficial for identifying the source of variance changes.


目錄
中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
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 3
1.4 研究方法與步驟 3
1.5 研究架構 4
第二章 文獻探討 6
2.1 多變量統計製程管制法 6
2.2 辨識多變量製程異常來源之人工智慧法 9
2.3 整體式學習法 11
第三章 研究方法 17
3.1 支援向量機之設計 17
3.2 整體式分類模型 21
3.3 整體式支援向量機之辨識系統 22
3.3.1 整體式辨識系統之建構方法 22
3.3.2 訓練樣本之產生 24
3.3.3 輸入向量之選取 26
3.3.4 分類模型之最佳化 27
第四章 效益評估 29
4.1 整體式支援向量機辨識系統之效益評估 (變動程度 ) 29
4.1.1 特徵向量之績效比較 30
4.1.2 分類模型個數之績效比較 33
4.2 整體式支援向量機分類模型之效益評估 (管制界限) 35
第五章 結論與未來研究 37
5.1 結論 37
5.2 未來研究發展 37
參考文獻 39


Alt, F. B., “Multivariate quality control,” Encyclopedia of Statistical Science, vol. 6, N. L. Johnson and S. Kotz, (Eds.), Wiley, New York (1985).
Breiman, L., “Bagging predictors,” Machine Learning, 24, 123-140 (1996).
Chang, C. C., and Lin, C. J., “LIBSVM: a library for support vector machines,” (2001). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cheng, C. S., and Tzeng, C. A., “A neural network model for detecting shifts in the process mean and variability,” Journal of Chinese Institute of Industrial Engineers, 11, 67-75 (1994).
Cheng, C. S., and Cheng, H. P., “Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines,” Expert System with Applications, 35, 198-206 (2008).
Cheng, Z. Q., Ma, Y. Z., and Bu, A. J., “Variance shift identification model of bivariate process based on LS-SVM pattern recognizer,” Communication in Statistics: Simulation and Computation, 40, 286-296 (2011).
Chinnam, R. B., “Support vector machines for recognizing shifts in correlated and other manufacturing processes,” International Journal of Production Research, 40, 4449-4466 (2002).
Dietterich, T. G., and Bakiri, G., “Solving multiclass learning problems via error-correcting output codes,” Journal of Artificial Intelligence Research, 2, 263–286 (1995).
Du, S., Lv, J., and Xi, L., “An integrated for on-line intelligent monitoring and identifying process variability and its application,” International Journal of Computer Integrated Manufacturing, 23, 529-542 (2010).
Freund, Y., and Schapire, R., “A decision-theoretic generalization of on-line learning and an application to boosting.” Journal of Computer and System Sciences, 55, 119-139 (1997).
Guh, R. S., and Shiue, Y. R., “An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts,” Computers and Industrial Engineering, 55, 475-493 (2008).
Hansen, L. K., and Salamon, P., “Neural network ensemble,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993-1001 (1990).

Hotelling, H., “Multivariate quality control-illustrated by the air testing of sample bombsights,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W, Hastay and W. A. Wallis, New York : McGraw –Hill, 111-184, (1947).
Hsu, C. W., and Lin, C. J., “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, 13, 415-425 (2002).
Hsu, C. W., Chang, C. C., and Lin, C. J., “A practical guide to support vector classification” (2003). Available at http://www.csie.ntu.edu.tw/~cjlin/
Jacobs, R. A., “Methods for combining expert’s probability assessments,” Neural Computation, 7, 867-888 (1995).
Lincoln, W., and Skrzypek, J., “Synergy of clustering multiple back propagation networks,” Advances in Neural Information Processing Systems, 2, 650-657 (1990).
Low, C., Hsu, C. M., and Yu, F. J., “Analysis of variations in a multi-variate process using neural networks,” International Journal of Advanced Manufacturing Technology, 22, 911-921 (2003).
Montgomery, D. C., Introduction to Statistical Quality Control, Wiley, New York (2009).
Rokach, L., “Ensemble-based classifiers,” Artificial Intelligence Review, 33, 1-39 (2010).
Schapire, R., “The strength of weak learnability,” Machine Learning, 5, 197-227 (1990).
Sun, R., and Tsung, F., “A kernel-based multivariate control chart using support vector methods,” International Journal of Production Research, 41, 2975-2989 (2003).
Surtihadi, J., Raghavachari, M., and Runger G., “Multivariate control charts for process dispersion,” International Journal of Production Research, 42, 2993-3009 (2004).
Vapnik, V. N., The Nature of Statistical Learning Theory, Springer Verlag, 2ed, New York (2000).
Wu, B., and Yu, J. B., “A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes,” Expert Systems with Applications, 37, 4058-4065 (2010).
Yu, J. B., and Xi, L., “A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes,” Expert System with Applications, 36, 909-921 (2009).
Yu, L., Yue, W., Wang, S., and Lai, K. K., “Support vector machine based multiagent ensemble learning for credit risk evaluation,” Expert System with Applications, 37, 1351-1360 (2010).
Zhou, L., Lai, K. K., and Yu, L., “Least squares support vector machines ensemble models for credit scoring,” Expert System with Applications, 37, 127-133 (2010).


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