Bayesian deep learning for system identification

Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent neural networks (RNNs). However, DNNs also have limitations. First, DNNs can easily overfit the training data due to the model complexity. Second, DNNs are normally regarded as black-box models, which lack interpretability and cannot be used for white-box modelling. In this thesis, we develop sparse Bayesian deep learning (SBDL) algorithms that can address these limitations in an effectivemanner.