Sparse Bayesian Deep Learning for Dynamic System Identification. (arXiv:2107.12910v2 [eess.SY] UPDATED)

This paper proposes a sparse Bayesian treatment of deep neural networks
(DNNs) for system identification. Although DNNs show impressive approximation
ability in various fields, several challenges still exist for system
identification problems. First, DNNs are known to be too complex that they can
easily overfit the training data. Second, the selection of the input regressors
for system identification is nontrivial. Third, uncertainty quantification of
the model parameters and predictions are necessary. The proposed Bayesian
approach offers a principled way to …

arxiv bayesian bayesian deep learning deep learning identification learning