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
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