X-bar and R control chart interpretation using neural computing

Abstract This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples Prom probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and to predict future points from processes which exhibit long-term or cyclical drift. The advantages and disadvantages of neural control charts compared with traditional statistical process control are discussed.

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