Support vector regression based residual MCUSUM control chart for autocorrelated process

DOI10.1016/J.AMC.2007.12.059zbMath1144.62366OpenAlexW1998202984MaRDI QIDQ945289

Limam Mohamed, Ben Khediri Issam

Publication date: 12 September 2008

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2007.12.059

zbMATH Keywords

artificial neural networksstatistical process controlsupport vector regression

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics in engineering and industry; control charts (62P30) Artificial intelligence (68T99)

Related Items (3)

Bivariate copula-based CUSUM charts for monitoring conditional nonlinear processes with first-order autocorrelation ⋮ One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes ⋮ Optimal inspection policy for three-state systems monitored by control charts

Cites Work

• On a kernel-based method for pattern recognition, regression, approximation, and operator inversion
• Tests for special causes with multivariate autocorrelated data
• Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes
• Effects of autocorrelation on control chart performance
• Multivariate Quality Control Chart for Autocorrelated Processes
• A kernel-distance-based multivariate control chart using support vector methods
• On SPC for Short Run Autocorrelated Data
• Support vector machines for recognizing shifts in correlated and other manufacturing processes
• Kernel distance-based robust support vector methods and its application in developing a robust K-chart

This page was built for publication: Support vector regression based residual MCUSUM control chart for autocorrelated process