Effects of heavy tailed distribution on statistical control charts and neural network based control charts
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Date
2009-06
Authors
Poh Ying, Lim
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Abstract
Artificial Neural Networks (ANN) had been used for the detection and classification of
patterns in control charts. It has been shown that neural network can detect smaller shifts
better than statistical control charts. However, nearly all studies in this area assume that
the in-control process data in the control charts follow a normal distribution. In our study,
we focus on the effects of heavy tailed distributions on the performance of neural
network based control chart and statistical control charts. Statistical control charts like
Shewhart X control chart , exponentially weighted moving average control chart
(EWMA) and Cumulative Sum control chart (CUSUM) are presented to make the
comparison ofthe effects of heavy tailed distribution with Neural network based control
chart. In our study, we used Dev C++ to develop and analyze neural network based
control chart. The criterion fo compare the performance of both types of control charts is
the average run length (ARL). From the results, the neural network is less robust than
the statistical based control charts which include Shewhart X control chart, EWMI\ and
CUSUM. Based on these reasons, we concluded that the neural network approach is less
appropriate to be applied as control charts.
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Keywords
Heavy tailed distribution , Statistical control charts , Network based control charts