TY - JOUR
T1 - Distinguishing between mean, variance and autocorrelation changes in statistical quality control
AU - Guo, Y.
AU - Dooley, K. J.
N1 - Funding Information:
AcknowledgmentsThis research has been partially supported by Honeywell Solid State Electronics Center, 3M Engineering Systems and Technology Laboratory, the University of Minnesota Graduate School, and the Minnesota Supercomputer Institute. We wish to give thanks to anonymous referees for their helpful comments.
PY - 1995/2
Y1 - 1995/2
N2 - In order to enhance the probability of correct quality diagnosis, it is useful to be able to identify the statistical manner in which the quality signal has changed, i.e. identify change structure. Specifically we wish to distinguish between changes in mean, variance and lag one autocorrelation. Because these change structures yield significant similarities in their corresponding output, a multistage decision tree is necessary. A multistage classification system with a neural network and quadratic discriminant functions is used, where neural network output is an a priori distribution for the Bayesian quadratic discriminant function. Experimental results show that this multistage decision strategy performs significantly better than its single stage counterpart, with an overall success rate of 84%.
AB - In order to enhance the probability of correct quality diagnosis, it is useful to be able to identify the statistical manner in which the quality signal has changed, i.e. identify change structure. Specifically we wish to distinguish between changes in mean, variance and lag one autocorrelation. Because these change structures yield significant similarities in their corresponding output, a multistage decision tree is necessary. A multistage classification system with a neural network and quadratic discriminant functions is used, where neural network output is an a priori distribution for the Bayesian quadratic discriminant function. Experimental results show that this multistage decision strategy performs significantly better than its single stage counterpart, with an overall success rate of 84%.
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U2 - 10.1080/00207549508930162
DO - 10.1080/00207549508930162
M3 - Article
AN - SCOPUS:0038037522
SN - 0020-7543
VL - 33
SP - 497
EP - 510
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 2
ER -