Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks

Modeling capabilities of two types of learning systems are compared: the naïve Bayesian classifier (NBC) and artificial neural networks (ANNs), based on their prediction errors and relative importance factors of input signals. Simulated and real industrial data were used. It was found that NBC can be an effective and, in some applications, a better tool than ANNs.