Identifying the Dynamics of a System by Leveraging Data from Similar Systems
Author:
Affiliation:
1. Purdue University,Elmore Family School of Electrical and Computer Engineering
2. Huazhong University of Science and Techonology,The School of Artificial Intelligence and Automation
3. Purdue University,School of Mechanical Engineering
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9866948/9867142/09867413.pdf?arnumber=9867413
Reference20 articles.
1. Near optimal finite time identification of arbitrary linear dynamical systems;sarkar;Proc International Conference on Machine Learning,2019
2. On the sample complexity of the linear quadratic regulator;dean;Foundations of Computational Mathematics,2019
3. Data-Driven Sparse System Identification
4. Finite sample system identification: Optimal rates and the role of regularization;sun;Proc Learning for Dynamics and Control Conference,2020
5. Non-Asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectories
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2. Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13
3. Learning Personalized Models with Clustered System Identification;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13
4. Moving Horizon Estimation for Discrete-Time Linear Time-Invariant Systems Using Transfer Learning;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13
5. TFNet: Few-shot identification of LTI systems based on convolutional neural networks;Journal of Process Control;2023-09
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