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

Yi Junsub, Prybutok Victor R. and Clayton Howard R.
Additional contact information
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

Stochastics and Quality Control, 2001, vol. 16, issue 1, 5-15

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.

Keywords: Statistical Quality Control; Statistical Process Control; Control Charts; Neural Networks; Simulation (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1515/EQC.2001.5

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