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

Detecting the Changes of Mean and Variance Using Artificial Neural Network Approach : the Investigation of Design Strategies

蘇仁德(Jen-Te Shu)

指導教授 ： 鄭春生

元智大學/工程學院/工業工程與管理學系/碩士(1997年)

摘要

本研究是以倒傳遞類神經網路為基礎，發展一個製程管制法，用來監視並
區分製程平均值及變異性之變化。本研究考慮之異常情形包含：(1)管制
平均值向上或向下移動；(2)製程變異性改變；(3)製程平均值向上(或向
下)移動及製程變異性改變。
本研究發展的類神經網路有單一型及整合型兩種。單一型網路是依照樣本
大小分別建立，而整合型網路則是以一個網路應用於不同樣本大小之情況
。在研究中，我們也針對發展類神經網路之設計策略進行深入之探討。類
神經網路之偵測成效是以正確辨認率和平均連串長度來評估。模擬之結果
顯示本研究發展之類神經網路在辨認率上優於過去之研究。

關鍵字

類神經網路 ； 平均連串長度 ； 正確辨認率

並列摘要

In this research, a control procedure based on artificial neural
networks for distinguishing the changes in mean and variance was
developed. The out-of-control conditions considered in this
research includes (1) an upward or downward shift in the process
mean; (2) a change in the process variability; (3) a shift (up
or down) in the process mean along with a change in the process
variability.

The types of neural network-based control procedures are developed
in this research. One is developed based on different sample sizes,
the other is developed independent sample size, The design
strategies are also investigation in this research. The
performance of the proposed neural network has been evaluated
based on the recognition accuracy and the average run length
(ARL). Simulation results show that the proposed control
procedure is better than other research in terms of recognition
accuracy.

並列關鍵字

artificial neural network ； average run length ； recognition accuracy

參考文獻

2. Bauer, P. and P. Hackl, "The use of MOSUMS for quality control," Technometrics, 20, 431-436 (1978).

3. Champ, C. W., and W. H. Woodall, "Exact results for Shewart control charts with supplementary run length," Technomtrics, 29, 393-399 (1987).

4. Chang, S. I. and C. A. Aw, "A nerual fuzzy control chart for detecting and classifying process mean shifts," International Journal of Production Research, 34,8, 2265-2278 (1996).

5. Cheng, C. S. and C. A. Tzeng, "A neural network approach for detecting shifts in the process mean and variability," Journal of Chincese Institute of Industrial Engineers, 11, 67-75 (1994).

6. Cheng, C. S., "A multi-layer neural network model for detecting changes in the process mean," Computers industrial Engineering, 28, 1, 51-61 (1995).