柔性解算應用於管制圖：設計策略之研究

Detecting the Mean Changes in Fuzzy Linguistic Variable Using Control Chart and Fuzzy Neural Network Approaches： the Investigation of Design Strategies

李嘉慧(Chia-Hui Lee)

指導教授 ： 鄭春生博士

元智大學/工程學院/工業工程與管理學系/碩士(1997年)

摘要

傳統計數值管制圖是以二分法來判斷產品品質。此方法在很多狀況下並
不適用，因為產品品質並不是明顯區分為合用或不合用，而是有許多中
間等級的產品，由於沒有充分利用中間等級的資訊，導致計數值管制圖
之績效比計量值管制圖差。過去之研究建議用中間等級來描述產品品質
，這些中間等級可以表示成語言項目的形式，之後再以模糊理論建立模
糊管制圖來管制以語言項目表示之數據。
本研究是以類神經網路為基礎，發展一個偵測計數值資料平均值變化之
管制程序。在研究中，產品品質是以模糊語言變數來表示，類神經網路
是以模糊集合代表值為輸入，用來管制製程平均值是否發生偏移。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探討。類神經網
路之效益是以平均連串長度來評估，模擬分析之結果顯示以類神經網路
對於製程平均值之偵測較模糊管制法為優。

關鍵字

統計製程管制 ； 類神經網路 ； 模糊理論 ； 計數值管制圖

並列摘要

Traditional attribut control charts judge the product quality
by the binary classification. This is not appropriate in many
situations where product quality does not change abruptly from
satisfactory to worthless, and there might be a number of
intermediate levels. Not fully utilize the intermediate
information results the in poorer performance than -chart.
Previous research proposes that the intermediate levels may
be expressed in the form of linguistic terms and then to use
the fuzzy set theory to construct the fuzzy attribute control
charts to monitor the data in linguistic form.
In this research, we propose a neural network-based quality
control procedure to improve the performance of the fuzzy
control charts. The primary issue focuses on the detection of
changes in the process mean shift. The imput of the neural
network is linguistic variable. We try to us the investigation
of design stratgies to improve the detecting. The neural
network performance is evaluated by on average run length.
An extensive simulation study indicates that the proposed
neural network approaches is better than fuzzy attribute
control charts.

並列關鍵字

Statistical Process Control ； Artificial Neural Network ； Fuzzy Set Theory ； Attributes Control Charts

參考文獻

1. Cheng, C. S., "Detecting changes in the process mean using artificial neural networks approach," Journal of Chinese Institute of Industrial Engineers, 11 , 1, 47-54 (1994).

3. Grant, E. W., and R. S. Leavenworth, Statistical Quality Control, McGraw-Hill Book Co., NY (1988).

4. Guo, Y. and K. J. Dooley, "Identification of change structure in statistical process control," International Journal of Production Research, 30, 1655-1669 (1992).

5. Hush, D. R., J. M. Salas, and B. Horne, "Error surfaces for multi-layer perceptrons, " IEEE Transactions on Systems, Man and Cybernetics, 22, 5, (1992).

6. Hush, D. R. and B. G. Horne, "Progress in supervised neural networks," IEEE Signal Processing Magazine, January, 8-39 (1993).