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Control chart patterns recognition is an important aspect of statistical process control (SPC). This paper provides a neural network-based pattern recognizer for the analysis of control patterns. This pattern recognizer looks for the following five nonrandom patterns: trend, systematic, cycle, mixture and shift.
In the past, various neural network-based approaches have been developed for the analysis of control chart patterns. In this research, we also develop a neural network-based pattern recignizer. However, the features extracted from process data were used as the network input instead of raw data.
The performance of the developed neural networks was evaluated by estimating the average run length and rate of correct classification (ROCC). Simulation results show that the pattern recognizer can recognize the control chart patterns correctly and fastly.
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