Automated recognition of control chart patterns for monitoring and diagnosing process quality has been an active area of research since the last 20 years. An artificial neural network (ANN) based models with back-propagation algorithm was known to have resulted the promising recognition accuracy. However, the performance of an ANN depends on a proper selection of the design parameters. In this paper, full factorial design of experiment (DOE) was utilized in investigating several parameters that influence the recognition accuracy of an ANN. This systematic method provided an optimal ANN design with satisfied recognition accuracy
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