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Cited by

• 2011On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model40 cited
• 2008Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach39 cited
• 2003Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes38 cited
• 2003An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters31 cited
• 2012Control charts for monitoring the autocorrelated process parameters: a literature review28 cited
• 2011Recognition of unnatural patterns in process control charts through combining two types of neural networks27 cited
• 2011Estimating the shift size in the process mean with support vector regression and neural networks22 cited
• 2008Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes22 cited
• 2017Identification of patterns in control charts for processes with statistically correlated noise21 cited
• 2005Environmental statistical process control using an augmented neural network classification approach21 cited
• 2010LRProbcontrol chart based on logistic regression for monitoring mean shifts of auto-correlated manufacturing processes20 cited
• 2020On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes