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ARL Comparisons Between Neural Network Models and -Control Charts for Quality Characteristics that are Nonnormally Distributed

Author

Listed:
  • Yi Junsub

    (Dept. of Management Information Systems, Kyungsung University, 110-1 Daeyeon-dong, Nam-gu, Pusan, 608-736, South Korea. junsub@star.kyungsung.ac.kr)

  • Prybutok Victor R.

    (University of North Texas, Denton TX 76203-5249. prybutok@unt.edu)

  • Clayton Howard R.

    (Auburn University, Auburn, AL 36849-5241. hclayton@business.auburn.edu)

Abstract

One widely used control chart, the -chart, is based on the assumption that means of samples drawn from the process are normally distributed. When the normality assumption is not valid, control chart users may choose from several different courses of action. These include using Box-Cox power transformations on the original data to yield an approximate normal distribution, increasing the size of the samples drawn from the process until the distribution of the sample means is considered normal, and modifying the -chart to employ asymmetric control limits instead of limits that are equidistant from the process target mean. Since none of the remedies for handling nonnormal processes is completely satisfactory, we build on previous neural network research by developing a neural network to control nonnormal processes. Comparison of the performance of our neural network model with that of traditional -control charts shows that the neural network model is superior to the traditional -control charts.

Suggested Citation

  • Yi Junsub & Prybutok Victor R. & Clayton Howard R., 2001. "ARL Comparisons Between Neural Network Models and -Control Charts for Quality Characteristics that are Nonnormally Distributed," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 5-15, January.
    • Handle: RePEc:bpj:ecqcon:v:16:y:2001:i:1:p:5-15:n:7
    DOI: 10.1515/EQC.2001.5
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    References listed on IDEAS

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    1. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
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