Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach
DOI10.1080/00207540601011501zbMath1147.68631OpenAlexW1975777064MaRDI QIDQ3518476
Publication date: 8 August 2008
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207540601011501
pattern recognitionneural networkslearning vector quantizationcontrol chartsstatistical process controlautocorrelated processes
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)
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Cites Work
- A neural network approach to identifying cyclic behaviour on control charts: a comparative study
- The effect of autocorrelation on the retrospectiveX-chart
- A neural fuzzy control chart for detecting and classifying process mean shifts
- A neural network approach for the analysis of control chart patterns
- Run-Length Distributions of Special-Cause Control Charts for Correlated Processes
- Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer
- Recognition of control chart concurrent patterns using a neural network approach
- An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study
- Simultaneous identification of mean shift and correlation change in AR(1) processes
- Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme
- Average run lengths for cusum control charts applied to residuals
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