跳到主要內容

臺灣博碩士論文加值系統

(18.97.14.86) 您好!臺灣時間:2025/02/08 02:54
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:黃啟原
研究生(外文):Chi-Yuan Huang
論文名稱:應用小波分析法與類神經網路建構管制圖非隨機樣式之辨識系統
論文名稱(外文):Control Chart Patterns Recognition using Wavelet Transfer and Neural Networks
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
畢業學年度:94
語文別:中文
論文頁數:64
中文關鍵詞:統計製程管制類神經網路Haar 離散小波轉換特徵值擷取
外文關鍵詞:statistical process controlneural networkHaar discrete wavelet transferextracted features
相關次數:
  • 被引用被引用:1
  • 點閱點閱:359
  • 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:1
管制圖是統計製程管制 (Statistical Process Control; SPC) 中之主要工具,它可用來判斷製程是否存在可歸屬原因所造成之變異。當製程存在可歸屬原因時,管制圖會呈現特定的非隨機性樣式變化,例如:趨勢樣式、偏移樣式、週期性樣式等。然而正確辨識出非隨機性樣式,可以縮小診斷製程可歸屬原因之範圍,有助於規劃改善對策。

本研究之主要目的為建立一個以類神經網路為基礎之辨識系統,用以偵測和辨識非隨機樣式的類型,以作為規劃製程矯正措施的依據。本研究首先探討如何利用Haar 離散小波多重解析擷取重要製程數據之特徵。製程數據經 Haar 離散小波轉換 (Discrete Wavelet Transfer; DWT) 後可獲得不同解析尺度下的係數,經由小波之數據轉換方式以呈現樣式的重要特徵。第二、本研究將建立一個監督式之類神經網路作為非隨機樣式的辨認系統。我們將考慮各類非隨機樣式同時存在與單一週期性樣式單獨發生之情形,評估原始數據和小波之特徵擷取方式,對於類神經網路之辨識績效的影響。
Control chart pattern recognition is an important work in statistical process control. A control chart can present many unnatural patterns: trends, sudden shifts, and cycles. The presence of unnatural patterns indicates that a process is affected by assignable causes, and corrective actions should be taken. This research will develop a neural network-based recognizer for control chart pattern recognition. First, we apply a multi-resolution analysis approach based on Haar discrete wavelet transfer (DWT) to extract distinguished features from raw data. The extracted features are used as the components of the input vectors. Secondly, we will develop a supervised neural network for control chart pattern recognition. In addition, we will focus on single cyclic pattern and concurrent patterns that can be characterized using this classifier. The performance of the neural network using features extracted from wavelet analysis as the components of the input vectors will be investigated and compared.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vii
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 4

第二章 文獻探討 6

2.1 傳統統計製程管制法 6
2.2 類神經網路於統計製程管制分析之應用 7
2.2.1 管制圖非隨機樣式之偵測及辨認 7
2.2.2 特徵擷取於製程數據之應用 9
2.3 小波轉換於統計製程管制之應用 12

第三章 小波分析 17

3.1 小波解析過程 17
3.2 Haar離散小波轉換之特徵擷取 19
3.3 小波逆轉換過程 22

第四章 類神經網路 25

4.1 類神經網路概述 25
4.2 倒傳遞類神經網路 27

第五章 非隨機樣式之辨識系統 31

5.1 訓練樣本的產生 31
5.2 類神經網路之輸入訊號 33
5.3 辨識系統之架構 34
5.4 效益評估 37
5.4.1 評估指標 37
5.4.2 評估方法 38

第六章 辨識系統之效益評估 41

6.1 學習樣本 41
6.2 網路建立與學習 43
6.3 實驗結果評估及分析 44
6.3.1 網路架構I之測試結果 45
6.3.2 網路架構II之測試結果 48

第七章 結論與未來研究 59

7.1 結論 59
7.2 未來研究 59

參考文獻 61
1.Al-Assaf Y., “Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks,” Computers & Industrial Engineering, 47, 17-29 (2004).
2.Aradhye, H. B., Bakshi, B. R., Strauss, R. A., and Davis, J. F., “Multisacle SPC using wavelets-theoretical analysis and properties,” AICHE Journal, 49, 939-958 (2003).
3.Assaleh K., and Al-Assaf, Y., ”Features extraction and analysis for classifying causable patterns in control charts,” Computers & Industrial Engineering, 49, 168-181 (2005).
4.Bakshi, B. R., “Multiscale analysis and modeling using wavelets,” Journal of Chemometrics, 13, 415-434 (1999).
5.Cheng, C. S., “A neural network approach for the analysis of control chart patterns,” International Journal of Production Research, 35, 667-697 (1997).
6.Cheng, C. S., and Cheng, H. P., “Control chart pattern recognition using wavelet analysis and neural networks,” in Proceedings The 6th Asia Pacific Industrial Engineering and Management Conference, December 4-7, 2005, Manila, Philippines.
7.Cheng, C. S., and Tzeng, C. A., “A backpropagation neural network for the identification of change structure in statistical process control,” Journal of the Chinese Institute of Industrial Engineers, 12, 215-223 (1995).
8.Cheng, C. S., and Tzeng, C. A., “A neural network approach for detecting shifts in the process mean and variability,” Journal of the Chinese Institute of Industrial Engineers, 11, 67-75 (1994).
9.Crone, S. F., Lessmann, S., and Stahlbock, R. “The impact of preprocessing on data mining: An evaluation of classifer sensitivity in direct marketing,” European Journal of Operational Research, 173, 781-800 (2006).
10.Dedeakayogullari, I., and Burnak, N., “The determination of mean and/or variance shifts with artificial neural networks,” International Journal of Production Research, 37, 2191-2200 (1999).
11.Donoho, D. L., “De-noising by soft-thresholding,” IEEE Transactions, 41, 613-627 (1995).
12.Donoho, D. L., and Johnstone, I. M., “Ideal spatial adaptation via wavelet shrinkage,” Biometrika, 81, 425-455 (1994).
13.Guh, R. S., and Hsieh, Y. C., “A neural network based model for abnormal pattern recognition of control charts,” Computers & Industrial Engineering, 36, 97-108 (1999).
14.Guo, Y., and Dooley, K. J., “Distinguishing between mean, variance and autocorrelation changes in statistical quality control.” International Journal of Production Research, 33, 497-510 (1995).
15.Guo, Y., and Dooley, K. J., “Identification of change structure in statistical process control.” International Journal of Production Research, 30, 1655-1669 (1992).
16.Hassan, A., Baksh, M., Shaharoun, A. M., and Jamaluddin, H., “Improved SPC chart pattern recognition using statistical features,” International Journal of Production Research, 41, 1587-1603 (2003).
17.Hush, D. R., and Horne, B. G., “Progress in supervised neural networks,” IEEE Signal Processing Magazine, January, 8-39 (1993).
18.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).
19.Hwarng, H. B., “Proper and effective training of a pattern recognizer for cyclic data,” IIE Transactions, 27, 746-756 (1995).
20.Jang, K. Y., Yang, K., and Kang, C., “Application of artificial neural network to identify non-random variation pattern on the run chart in automotive assembly process,” International Journal of Production Research, 41, 1239-1254 (2003).
21.Mallet, Y., Coomans, D., and de Vel, O., “Recent development in discriminant analysis on high dimensional spectral data,” Chemometrics and Intelligent Laboratory Systems, 35, 157-173 (1996).
22.Nelson. L. S., “The Shewhart control chart-tests for special cause,” Journal of Quality Technology, 16, 237-239 (1984).
23.NeuralWare Professional II/Plus (1995). Neural Computing: A Technology Handbook for Professional II/Plus and NeuralWorks Explorer. Pittsburgh: NeuralWare, Inc.
24.Pham, D. T., and Oztemel, E., “Control chart pattern recognition using neural networks,” Journal of Systems Engineering, 2, 256-262 (1992).
25.Pham, D. T., and Wani, M. A., “Feature-based control chart pattern recognition,” International Journal of Production Research, 35, 1875-1890 (1997).
26.Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986). Learning Internal Representations by Error Propagation. In Parallel Distributed Processing (Edited by D. E. Rumelhart and J. L. McClelland), Vol. 1, 318-362. MIT Press, Cambridge, MA.
27.Smith, A. E., “X-bar and R control interpretation using neural computing,” International Journal of Production Research, 32, 309-320 (1994).
28.Western Electric Company, Statistical Quality Control Handbook, Western Electric Co. Inc., Indianapolis, Indiana (1958).
29.林榮和,「應用類神經網路於管制圖非隨機性樣式之辨識」,元智大學工業工程與管理所碩士論文 (1999)。
30.陳信嘉,「管制圖非隨機樣式之辨識及參數之估計」,元智大學工業工程與管理所碩士論文 (1999)。
31.蔡政良,「以特徵為基之管制圖非隨機性樣式的辨識-使用類神經網路」,元智大學工業工程與管理所碩士論文 (1996)。
32.羅宗元,「應用小波分析與類神經網路於管制圖非隨機樣式之偵測」,元智大學工業工程與管理所碩士論文 (2005)。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊