X̄ control chart pattern identification through efficient off-line neural network training

Abstract

Back-propagation pattern recognizers (BPPR) are proposed to identify unnatural pattern exhibited on Shewhart control charts. In this paper an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated x data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR.