Bayesian inference for neural network based high-precision modeling

In this paper we implement neural network structure and Bayesian inference in order to improve performance black-box modeling for unknonw nonlinear systems. This kind of structure works in batch form passing both the identification and the statistical training. Two nonlinear systems and two data sets of seismic information from regions of Italy and Mexico are used to evaluate the methods. The results are satisfied.