Moving-window spectral neural-network feedforward process control

Unlike reactive feedback control, feedforward control is a proactive method by which information about a measurable disturbance is fed, ahead of time, to the manipulated inputs of a process, the output of which is to be controlled, so as to counteract the effect of the disturbance. Discretized observations on the process variable are indexed to form a time series. A time-series model is fitted to the series. The ultrahigh signal-to-noise ratio fitted values are examined by a neural network, for patterns which detect when the future process is expected to become out of control. The neural-network diagnosis forms the basis for corrective action, prior to the process becoming out of control. In principle, this goes beyond SPC to achieve a process which is never actually out of control