Learning a Nonlinear Model of a Manufacturing Process Using Multilayer Connectionist Networks

A connectionist (neural) network learns a nonlinear process model by observing a simulated manufacturing process in operation. The objective is to use the model to estimate the effects of different control strategies, removing the experimentation from the actual process. Previously we demonstrated that a linear, single-layer connectionist network can learn a model as accurately as a conventional. linear regression technique, with the advantage that the network processes data as it is sampled. Here we present experiments with a multilayer extension of the network that learns a nonlinear model.