Simultaneous identification of mean shift and correlation change in AR(1) processes
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Publication:4681145
DOI10.1080/00207540512331311822zbMath1089.93021OpenAlexW2071521303MaRDI QIDQ4681145
Publication date: 14 June 2005
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207540512331311822
neural networksautocorrelationback-propagationstatistical process controlaverage run lengthsimultaneous identification
Applications of statistics in engineering and industry; control charts (62P30) Stochastic systems in control theory (general) (93E03)
Related Items (4)
Shift detection and source identification in multivariate autocorrelated processes ⋮ Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach ⋮ Toward identifying the source of mean shifts in multivariate SPC: a neural network approach ⋮ Using anMQEchart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes
Cites Work
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- Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters
- An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study
- Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme
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