Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyperparameter Estimator-Reference-Cited by-同舟云学术

Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyperparameter Estimator

Published:2023-12 Issue:12 Volume:68 Page:7224-7239
ISSN:0018-9286
Container-title:IEEE Transactions on Automatic Control
language:
Short-container-title:IEEE Trans. Automat. Contr.

Author:

Ju Yue¹^ORCID,Mu Biqiang²^ORCID,Ljung Lennart³^ORCID,Chen Tianshi¹^ORCID

Affiliation:

1. School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China

2. Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing, China

3. Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Council

Central Government of China

Vetenskapsrådet

Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9/10345385/10077740.pdf?arnumber=10077740

Reference34 articles.

1. On the mathematical foundations of stable RKHSs

2. On kernel design for regularized LTI system identification

3. On semiseparable kernels and efficient implementation for regularized system identification and function estimation

4. System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques

5. Implementation of algorithms for tuning parameters in regularized least squares problems in system identification

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1. On convergence of covariance matrix of empirical Bayes hyper-parameter estimator;Control Theory and Technology;2024-04-18

2. When cannot regularization improve the least squares estimate in the kernel-based regularized system identification;Automatica;2024-02

3. A Family of Hyper-parameter Estimators for Regularized Linear System Identification;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

4. An Efficient Implementation for Kernel-Based Regularized System Identification with Periodic Input Signals;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13