Fast and Accurate Recognition of Control Chart Patterns Using a Time Delay Neural Network_

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題名：	Fast and Accurate Recognition of Control Chart Patterns Using a Time Delay Neural Network
書刊名：	工業工程學刊
作者：	顧瑞祥／薛友仁
作者(外文)：	Guh, Ruey-shiang／Shiue, Yeou-ren
出版日期：	2010
卷期：	27:1
頁次：	頁61-79
主題關鍵詞：	時間延遲神經網路；形狀辨識；管制圖；統計製程管制；Time delay neural network；Pattern recognition；Control chart；Statistical process control
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:39

Pattern recognition is a critical issue in statistical process control because unnatural patterns displayed by control charts can be associated with specific causes that adversely impact on the manufacturing process. Recently, neural networks have been widely investigated as an effective approach to control chart pattern (CCP) recognition. However, most of the research in this field has used traditional back propagation networks (BPNs) that cannot deal with patterns that vary over time in an online CCP recognition scheme. This causes a pattern misclassification problem in nearly all neural network-based studies in the field of online CCP recognition. The present article presents a novel application of utilizing a time delay neural network (TDNN)-based model to address this problem. The TDNN, with its special architecture, can represent relationships between patterns in a time sequence, and is, therefore, suitable to be trained with dynamic patterns that change over time. Numerical results indicate that the pattern misclassification problem has been addressed effectively by the proposed TDNN-based model. When compared with traditional BPNs, the TDNN model has better performance in both recognition accuracy and speed. In comparison with traditional control chart approaches, the proposed model is capable of superior performance of average run length, while the category of the unnatural CCP can also be accurately identified.

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期刊論文
1.	Guh, R. S.(2007)。On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach。Quality and Reliability Engineering International，23(3)，367-385。
2.	Cheng, C.-S(1997)。A neural network approach for the analysis of control chart patterns。International Journal of Production Research，35(3)，667-697。
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6.	Lucas, J. M.(1982)。Combined Shewhart-CUSUM quality control schemes。Journal of Quality Technology，14(2)，51-59。

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圖書論文
1.	Rumelhart, D. E.、Hinton, G. E.、Williams, R. J.(1986)。Learning Internal Representations by Error Propagation。Parallel distributed processing: explorations in the microstructure of cognition。Cambridge, MA：MIT Press。

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