Sparse Bayesian deep learning for dynamic system identification-Reference-Cited by-同舟云学术

Sparse Bayesian deep learning for dynamic system identification

Published:2022-10 Issue: Volume:144 Page:110489
ISSN:0005-1098
Container-title:Automatica
language:en
Short-container-title:Automatica

Author:

Zhou Hongpeng,Ibrahim Chahine,Zheng Wei Xing,Pan Wei

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference38 articles.

1. Practical Gauss-Newton optimisation for deep learning;A. Botev,2017

2. Ayala, H. V. H., da Cruz, L. F., & Freire, R. Z., et al. 2014. Cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks. In Proceedings of the 2014 IEEE symposium on computational intelligence in control and automation (CICA), (pp. 1–7).

3. Nonlinear state-space identification using deep encoder networks;Beintema,2021

4. The Gauss-Newton matrix for deep learning models and its applications;Botev,2020

5. Convex optimization;Boyd,2004

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploiting the capacity of deep networks only at the training stage for nonlinear black-box system identification;Information Sciences;2025-01

2. A Bayesian Neural ODE for a Lettuce Greenhouse;2024 IEEE Conference on Control Technology and Applications (CCTA);2024-08-21

3. Datenbasierter Regler- und Beobachterentwurf mit neuronalen kanonischen Strukturen;at - Automatisierungstechnik;2024-08-01

4. Parameter identification of dynamical systems based on short-term prediction by the generalized cell mapping method with deep learning;Nonlinear Dynamics;2024-07-22

5. On Sparse Nonlinear System Identification Using Orthogonal Matching Pursuit, Orthogonal Least Squares and LASSO;2024 32nd Mediterranean Conference on Control and Automation (MED);2024-06-11