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Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model
16 references·0 citing·38 second hop
Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model (2014)Simultaneous monitoring of mean vector an…Statistical Methods for Quality Improvement (2011)Statistical Methods for Quali…Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques (2011)Online monitoring and fault i…An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts (2008)An effective application of d…Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines (2007)Identifying the source of var…On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach (2006)On‐line Identification and Qu…A New Multivariate Control Chart for Monitoring Both Location and Dispersion (2005)A New Multivariate Control Ch…Artificial neural networks to classify mean shifts from multivariate χ2 chart signals (2004)Artificial neural networks to…Proceedings of the International Joint Conference on Neural Networks, 2003. (2003)Proceedings of the Internatio…Mean shifts detection and classification in multivariate process: a neural-fuzzy approach (2002)Mean shifts detection and cla…Statistical Methods for Quality Improvement (2000)Statistical Methods for Quali…Kollár Ádám Ferenc levelezése (2000)Kollár Ádám Ferenc levelezéseAn integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study (1999)An integrated neural network …A neural network approach to characterize pattern parameters in process control charts (1999)A neural network approach to …A neural network approach for the analysis of control chart patterns (1997)A neural network approach for…X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING (1993)X¯ CONTROL CHART PATTERN IDEN…Back-propagation pattern recognizers for control charts: Methodology and performance (1993)Back-propagation pattern reco…The Shewhart Control Chart—Tests for Special Causes (1984)Interpreting Shewhart X Control Charts (1985)A neural fuzzy control chart for detecting and classifying process mean shifts (1996)Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters (1998)A hybrid learning-based model for on-line detection and analysis of control chart patterns (2005)A Multivariate Exponentially Weighted Moving Average Control Chart (1992)Optimal designs of the multivariate synthetic chart for monitoring the process mean vector based on median run length (2011)A Multivariate Sign Chart for Monitoring Process Shape Parameters (2013)Multivariate Quality Control (2013)A Control Chart for the Multivariate Coefficient of Variation (2015)Multivariate Quality Control (1947)Decomposition of T 2 for Multivariate Control Chart Interpretation (1995)A kernel-distance-based multivariate control chart using support vector methods (2003)Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach (2011)Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines (2013)Support vector machine in statistical process monitoring: a methodological and analytical review (2016)Statistical Quality Control (1980)A neural network based model for abnormal pattern recognition of control charts (1999)A control chart pattern recognition system using a statistical correlation coefficient method (2005)Simulation modelling and analysis (1983)On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines (2012)Statistics for Experimenters (1979)A review of multivariate control charts (1995)Research Issues and Ideas in Statistical Process Control (1999)Quality Control and Industrial Statistics. (1967)Multivariate SPC Charts for Monitoring Batch Processes (1995)A Review and Perspective on Control Charting with Image Data (2011)Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014)Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data (2015)A review of neural networks for statistical process control (1998)A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme (2012)Control chart pattern recognition using the convolutional neural network (2019)The CUSUM and the EWMA Head-to-Head (2014)Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation (2015)Phase I Distribution-Free Analysis of Multivariate Data (2017)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong (2020)Stock market movement forecast: A Systematic review (2020)
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References

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  • 2000Statistical Methods for Quality Improvement750 cited
  • 2000Kollár Ádám Ferenc levelezése613 cited
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  • 1993X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING104 cited
  • 2005A New Multivariate Control Chart for Monitoring Both Location and Dispersion90 cited
  • 2007Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines90 cited
  • 2006On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach85 cited
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  • 2004Artificial neural networks to classify mean shifts from multivariate χ2 chart signals72 cited
  • 2002Mean shifts detection and classification in multivariate process: a neural-fuzzy approach67 cited
  • 1999An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study

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67 cited
  • 2008An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts62 cited
  • 2011Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques59 cited
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