| Abstract: | Acceptance sampling, a category of statistical quality control, deals with the confidence of the product's quality. In certain times, it is necessary to deal with the error in the demanding distribution counting on the sample size and the pertained population size, in determining the necessitated sample size for the acute exactitude. Further this sample size with minimized error is utilized in deriving the most beneficial OC curve. Neural networks have been used to train the data with the resulting error and their matching toleration level for the sample sizes of different population sizes. This trained network can be used to foster automated acceptance or rejection of the sample size to be used for a better OC curve based on the minimized error, ensuing time reduction of the burdened work. It is better explained in this paper with the geo-statistics data, using SAS program. |
