Bayesian-based on-line applicability evaluation of neural network models in modeling automotive paint spray operations

The neural network (NN) models well trained and validated by the same data may exhibit noticeably different predictabilities in applications. This is mainly due to the fact that the knowledge captured by the NNs in training may be different in depth and breadth. In this regard, using a set of nearly equally superior models, instead of a single one, may demonstrate its robustness of system performance prediction in on-line application. An unresolved issue, then, is how to value the prediction by each model of the model set in each application step. In this paper, we introduce a Bayesian-based model-set management method for constructing a statistically superior model set for on-line a t o t p ©