Bayesian deep learning for system identification

Doctoral thesis (2022)

Authors

H. Zhou Robot Dynamics - Mechanical, Maritime and Materials Engineering
Research Group
Robot Dynamics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 H. Zhou
DOI: https://doi.org/10.4233/uuid:94b0cdd5-280b-4afb-a210-f19ecf12cf66
Deep learning System identification Hessian calculation Sparse Bayesian learning Symbolic regression Neural architecture search Network compression
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Robot Dynamics

Abstract

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.