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 application. Specifically, this method is for manipulating the model set by assigning statistically most appropriate weights to the model predictions; the weighted model predictions determine overall system behavior. A repeated use of the method keeps the weights updated constantly based on the newly available system data, which makes the model-set-based system performance description more precise and robust. The efficacy of the method is demonstrated by studying an automotive paint spray process where the thin film thickness on vehicle surface should be precisely predicted.