X-bar andRcontrol chart interpretation using neural computing
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Publication:4394201
DOI10.1080/00207549408956935zbMath0911.90193OpenAlexW2028519536MaRDI QIDQ4394201
Publication date: 11 June 1998
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207549408956935
Related Items (18)
Fuzzy quality feature monitoring model ⋮ Shift detection and source identification in multivariate autocorrelated processes ⋮ Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme ⋮ Quality control using convolutional neural networks applied to samples of very small size ⋮ Robust kernel distance multivariate control chart using support vector principles ⋮ Integrated Application of SPC/EPC/ICA and neural networks ⋮ Identification and interpretation of manufacturing process patterns through neural networks ⋮ Multilayer perceptions for detecting cyclic data on control charts ⋮ A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes ⋮ An integrated approach for process monitoring using wavelet analysis and competitive neural network ⋮ Using anMQEchart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes ⋮ Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters ⋮ Support vector machines for recognizing shifts in correlated and other manufacturing processes ⋮ A bibliography of neural network business applications research: 1994--1998 ⋮ Recognition of unnatural patterns in manufacturing processes using the minimum description length criterion ⋮ A neural network approach to identifying cyclic behaviour on control charts: a comparative study ⋮ Comparison of Novelty Score-Based Multivariate Control Charts ⋮ A neural fuzzy control chart for detecting and classifying process mean shifts
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