(3.238.249.17) 您好!臺灣時間:2021/04/13 19:13
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:邱玉琳
研究生(外文):Yu-Lin Chiu
論文名稱:應用類神經網路與支援向量機於多變量自我相關製程變異來源之辨識
論文名稱(外文):Identifying the Source of Multivariate Autocorrelated Process Shiftsby Artificial Neural Networks and Support Vector Machine
指導教授:鄭春生鄭春生引用關係
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:2007
畢業學年度:95
語文別:中文
論文頁數:76
中文關鍵詞:多變量製程管制自我相關性類神經網路支援向量機統計距離
外文關鍵詞:multivariate process controlautocorrelationartificial neural networksupport vector machinestatistical distance
相關次數:
  • 被引用被引用:2
  • 點閱點閱:191
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:0
實際工業製程中,連續生產過程、自動化生產以及檢驗等等,將導致製程數據間存在自我相關性,且現今同時監控數個品質特性已成了必然的趨勢。過去研究中,多變量管制法主要在偵測多變量製程異常之發生,進而發展出有效之監控系統,可及早偵測到製程之變化。然而在多變量自我相關製程中,傳統 T-square 管制法卻無法的偵測到異常狀況。本研究以 Hotelling T-square 管制法判斷多變量自我相關製程平均值是否發生異常,利用類神經網路及支援向量機兩種分類方法對已發生異常之製程進行異常來源之分類,以判斷發生異常之變量為何。
本研究使用多變量自我相關製程中三種模型,並探討不同共變異數矩陣和自我相關參數矩陣之平均值偏移型態。此外,除了以原始數據做為分類法之輸入向量,也考慮以統計距離做為變量之特徵值,比較原始數據與特徵值之績效。以正確分類率作為各分類法之評估指標,觀察各分類法對於異常來源分類之辨識能力。結果顯示,特徵值統計距離對各分類法均提升不少效益,表示統計距離對本研究具有可行性。且支援向量機之正確分類率相對類神經網路具有較佳辨識能力。
In many industrial processes, a product may have two or more related quality characteristics which should be monitored simultaneously. However, the measurement data from many manufacturing processes are not independent in practice. Thus, the traditional T-square chart was insufficient for detecting mean shifts in multivariate auto-correlated processes. The Hotelling’s T-square control chart has been designed for detecting mean shifts. In this research, we purposed two mean shifts classifiers based on artificial neural network (ANN) and support vector machine (SVM). When an out-of-control signal is appeared, the classifier will determine which variable is responsible for the mean shifts.
In this research, we considered three models of multivariate auto-correlated process. Various shift scenarios expressed in covariance matrices and autocorrelation parameter matrices were investigated. Statistical distance was proposed to be used as the component of the input vectors. The performance of the proposed method was evaluated by computing correct classification accuracy. The results showed that the proposed approach is a successful method in identifying the source of mean change in multivariate auto-correlated process. Results from our experiment also indicated that SVM-based classifier performs better than the neural network-based classifier.
摘要.................................................. i
英文摘要.............................................. ii
致謝.................................................. iii
目錄.................................................. iv
表目錄................................................ vi
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..................................... 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 時間序列模型................................. 8
2.3 類神經網路於偵測製程平均值之應用............. 9
2.4 支援向量機之應用............................. 10
第三章 研究方法..................................... 12
3.1 多變量T-square管制圖......................... 12
3.2 類神經網路之基本理論......................... 15
3.2.1 倒傳遞網路之基本架構........................... 17
3.2.2 倒傳遞網路之運作............................... 18
3.3 支援向量機基本理論........................... 20
3.3.1 線性支援向量機................................. 22
3.3.2 非線性支援向量機............................... 25
3.4 向量自我迴歸模型............................. 27
3.5 統計距離..................................... 30
第四章 類神經網路與支援向量機之參數設定............. 32
4.1 訓練樣本的產生............................... 32
4.2 類神經網路................................... 35
4.2.1 類神經網路之輸入輸出向量....................... 35
4.2.2 類神經網路之架構............................... 36
4.2.3 類神經網路訓練樣本............................. 37
4.2.4 類神經網路之訓練............................... 38
4.3 支援向量機................................... 38
4.3.1 支援向量機之輸入輸出向量與訓練樣本............. 38
4.3.2 支援向量機之架構............................... 39
第五章 效益評估..................................... 41
5.1 評估方法及評估指標........................... 41
5.2 實驗結果..................................... 42
5.2.1 模型I之結果 .................................... 43
5.2.2 模型II之結果................................... 55
5.2.3 模型III之結果.................................. 63
第六章 結論與未來研究............................... 71
6.1 結論......................................... 71
6.2 未來研究..................................... 72

參考文獻.............................................. 73
1.Alt, F. B., “Multivariate quality control,” Encyclopedia of statistical science 6S. Kotz ad N. L. Johnson, eds., John Wiley & Sons, New York (1985).
2.Alwan, L. C., and Radson, D., “ Time-series investigation of subsample mean charts,” IIE Transactions, 24, 66-80 (1992).
3.Box, G.. E. P., Jenkins, G.. M. and MacGregor, J. F., “Some recent advance in forecasting and control, Part II,” Journal of the Royal Statistical Society, Ser.C, 23, 158-179 (1974).
4.Charnes, J. M., “Tests for special causes with multivariate autocorrelated data,” Computers and operations research, 22, 4, 443-453 (1995).
5.Chen, L. H. and Wang, T. Y., “Artificial neural networks to classify mean shifts from multivariate x2 chart signals,” Computer & industrial engineering, 47, 195-205 (2004).
6.Cheng, C. S., “A multi-layer neural network model for detecting changes in the process mean,” Computers and Industrial Engineering, 28, 51-61 (1995).
7.Chinnam, R. B., “Support vector machines for recognizing shifts in correlated and other manufacturing processes,” International Journal of Production Research, 40, 4449-4466 (2002).
8.Fletcher, R., Practical methods of optimization, Wiley, New York (2000).
9.Harris, T. J., and Ross, W. H. “Statistical process control procedures for correlated observations,” The Canadian Journal of Chemical Engineering, 69, 48-57 (1991).
10.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).
11.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).
12.Hush, D. R. and Horne, B. G. “Progress in supervised neural networks,” IEEE Signal Processing Magazine, January, 8-39 (1993).
13.Hush, D. R., Salas, J. M. and Horne, B. G. “Error surfaces for multi-layer perceptrons,” IEEE Transactions on System, Man and Cybernetics, 22, 2 (1992).
14.Hwarng, H. B., “Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme,” International Journal of Production Research, 42, 573-595 (2004).
15.Johnson, R. A., and Wichern, D. W., Applied Multivariate Statistical Analysis, Englewood Cliffs, New Jersey (1992).
16.Kalgonda, A. A., and Kulkarni, S. R., “Multivariate quality control chart for autocorrelated processes,” Journal of Applied Statistics, 31, 3, 317-327 (2004).
17.Mahalanobis, P. C., “On the generalized distance in statistics,” Proceedings of the National Institute of Science of India, 12, 49-55 (1936).
18.Mastrangelo, C. M., and Forrest, D. R., “Multivariate autocorrelated processes: data and shift generation,” Journal of Quality Ttechnology, 34, 2, 216-220 (2002).
19.Montgomery, D. C., and Mastrangelo, C. M. “Some statistical process control methods for auto-correlated data,” Journal of Quality Technology, 23, 3, 179-193 (1991).
20.Montgomery, D. C., Introduction to Statistical Quality Control, Wiley, New York (2005).
21.Morrison, D. F., Multivariate statistical methods, McGraw-Hill (1990).
22.NeuralWare Professional II/Plus (1997). Neural Computing: A Technology Handbook for Professional II/Plus and NeuralWorks Explorer. Pittsburgh: NeuralWare, Inc.
23.Pugh, G. A., “A comparison of neural networks to SPC charts,” Computers and Industrial Engineering, 21, 253-255 (1991).
24.Ribeiro, B., “Support vector machines for quality monitoring in a plastic injection molding process,” IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, 35, 401-410 (2005).
25.Rodriguez, J. J., Alonso, C. J., and Maestro, J. A., “Support vector machines of interval-based features for time series classification,” Knowledge-Based Systems, 18, 171-178 (2005).
26.Runger, G. C., Alt, F. B., Montgomery, D. C., “Contributors to a multivariate statistical process control signal,” Communications in Statistics- Theory and Methods, 25, 2203-2213 (1996).
27.STATISTICA (2003). Statistica Data Miner. OK: StatSoft, Inc.
28.Sun, R., and Tsung, F., “A kernel-distance-based multivariate control chart using support vector methods,” International Journal of Production Research, 41, 2975-2989 (2003).
29.Surtihadi, J., Raghavachari, M. and Runger, G., “Multivariate control charts for process dispersion,” International Journal of Production Research, 42, 2993-3009 (2004).
30.Timm, N. H., “Multivariate quality control using finite intersection tests,” Journal of Quality Technology, 28, 233-243 (1996).
31.Vapnik, V. N., Statistical learning theory, Wiley, New York (1998).
32.Vapnik, V. N., The Nature of Statistical Learning Theory, Springer, New York (2000).
33.Wardell, D. G., Moskowitz, H. and Plante, R. D. “Control charts in the presence of data correlation,” Management Science, 38, 1084-1105 (1992).
34.Woodall, W. H. and Ncube, M. M., “Multivariate CUSUM quality control procedures,” Technometrics, 27, 285-292 (1985).
35.Zhang, N. F., “A statistical control chart for stationary process data,” Technometrics, 40, 24-38 (1998).
36.Zhang, N. F., “Detection capability of residual control chart for stationary process data,” Journal of Applied Statistics, 24, 475-492 (1997).
37.Zorriassatine, F., and Tannock, J. D. T., “A review of neural networks for statistical process control,” Journal of Intelligent Manufacturing, 9, 209-224 (1998)
38.Zorriassatine, F., Tannock, J. D. T. and O’Brien, C., “Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes,” Computer & industrial engineering, 44, 385-408 (2003).
39.阮冰如,「應用類神經網路與支援向量機於多變量製程變異來源之辨識」,元智大學工業工程與管理所碩士論文,2006。
40.楊慧萍,「以類神經網路建立偵測自我相關製程平均值偏移和參數估計之雙邊管制法」,元智大學工業工程與管理所碩士論文,2005。
41.蕭宇翔,「應用MTS於非平衡資料分析之穩健性研究-以行動電話檢測流程為例」,國立交通大學工業工程與管理所碩士論文,2005。
42.蘇朝墩,品質工程,中華民國品質學會,2002。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊
 
系統版面圖檔 系統版面圖檔