Application of ANN to Monitor the Correlated Process using Higher Sample Size

摘要

The Average run lengths (ARLs) of the modified X chart are computed by simulation using MATLAB software in this paper. The modified X chart, based on sum of chi-squares theory is able to counter the autocorrelation in the observations. Various optimal schemes of modified X chart for sample size (n) of 10 are proposed at different levels of correlation (Φ). The ARLs of the modified X chart are also validated and compared with the ARLs obtained by Artificial Neural networks (ANNs). It is found that when the level of correlation (Φ) increases for a particular sample size (n), the performance of all the schemes deteriorates. It is concluded that the modified X chart offers more robustness compared to Shewhart X chart for autocorrelated data at various levels of correlation (Φ) and shifts in the process mean.