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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
