Application of ANN to Monitor the Correlated Process using Higher Sample Size
Volume 11, Number 4, July 2015 - Paper 9 - pp. 395-404
D. R. PRAJAPATI1 and SUKHRAJ SINGH21 PEC University of Technology, Sector- 12, Chandigarh, INDIA
2 ACET Amritsar, Punjab, INDIA
(Received on October 13, 2013, revised on May 17 and May 26, 2014)
The Average run lengths (ARLs) of the modified x̄ chart are computed by simulation using MATLAB software in this paper. The modified x̄ chart, based on sum of chi-squares theory is able to counter the autocorrelation in the observations. Various optimal schemes of modified x̄ chart for sample size (n) of 10 are proposed at different levels of correlation (Φ). The ARLs of the modified x̄ 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 chart offers more robustness compared to Shewhart x̄ chart for autocorrelated data at various levels of correlation (Φ) and shifts in the process mean.
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