Reinforcement learning and approximate Bayesian computation (RL-ABC) for model selection and parameter calibration of time-varying systems-Reference-Cited by-同舟云学术

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Reinforcement learning and approximate Bayesian computation (RL-ABC) for model selection and parameter calibration of time-varying systems

Published:2023-10 Issue: Volume:200 Page:110458
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Ritto T.G.^ORCID,Beregi S.^ORCID,Barton D.A.W.^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference36 articles.

1. Digital twins: State-of-the-art and future directions for modelling and simulation in engineering dynamics applications;Wagg;ASME,2020

2. Digital twin, physics-based model, and machine learning applied to damage detection in structures;Ritto;Mech. Syst. Signal Process.,2021

3. Adaptive Control: Algorithms, Analysis and Applications;Landau,2011

4. Reinforcement Learning: An Introduction;Sutton,2018

5. Statistical and Computational Inverse Problems;Kaipio,2004

Cited by 1 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. Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey;WIREs Data Mining and Knowledge Discovery;2024-08-18

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