Improving neural network's performance using Bayesian inference.

Neural networks can be used as a data-driven model for system identification. But the probability properties of the training data are not included. The Bayesian approach can model the input and output probability distribution, but it cannot give points estimation. In this paper, we propose a special neural model, which combines the neural model and Bayesian inference. The probability distributions of input and output are obtained by the Bayesian approach. This statistical model changes the neural network's structure and improve the accuracy of the neural modeling. We also propose a neural network training method using Bayesian inference. The approach capabilities are analyzed. We use three examples to compare our method with the other black box methods. The results show that this new model is much better, when there are large noises, and the dynamics of the system is complex. [ABSTRACT FROM AUTHOR]