Synthetic neural networks for process control
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Abstract Synthetic neural networks offer great promise for process control. A performance comparison is drawn between traditional statistical process control methods and neural networks. Specifically, a series of simulation experiments in which back propagation networks are contrasted with control charts is described. The basis for comparison is average run length (both predicted and observed) and accuracy. The Monte Carlo simulations are derived from plausible production process data. Neural networks were found to perform reasonably well under most conditions.