A Family of Hyper-parameter Estimators for Regularized Linear System Identification-Reference-Cited by-同舟云学术

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A Family of Hyper-parameter Estimators for Regularized Linear System Identification

Published:2023-12-13 Issue: Volume:15 Page:6236-6241
ISSN:
Container-title:2023 62nd IEEE Conference on Decision and Control (CDC)
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Author:

Zhang Meng¹,Chen Tianshi²,Mu Biqiang¹

Affiliation:

1. Institute of Systems Science, Academy of Mathematics and System Science, Chinese Academy of Sciences,Key Laboratory of Systems and Control of CAS,Beijing,China,100190

2. School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong,Shenzhen,China,518172

Funder

National Key R&D Program of China

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10383192/10383193/10384202.pdf?arnumber=10384202

Reference21 articles.

1. A new kernel-based approach for linear system identification

2. On the estimation of transfer functions, regularizations and Gaussian processes—Revisited

3. Kernel methods in system identification, machine learning and function estimation: A survey

4. Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator

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

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