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Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

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References

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2005A hybrid learning-based model for on-line detection and analysis of control chart patterns103 cited

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2007Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines90 cited

Cited by

2021Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective52 cited
2014Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies48 cited
2013Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble42 cited
2014A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine40 cited
2012Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine36 cited
2021Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine29 cited
2021On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest28 cited
2019A control chart pattern recognition system for feedback-control processes26 cited
2016Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process24 cited
2015A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering22 cited
2017Identification of patterns in control charts for processes with statistically correlated noise21 cited
2013Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples19 cited
2020Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process18 cited
2022An SVM-GA based monitoring system for pattern recognition of autocorrelated processes16 cited
2018Recognition of control chart patterns in auto-correlated process based on random forest15 cited
2023Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns12 cited