Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization-Reference-Cited by-同舟云学术

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Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization

Published:2024-08 Issue: Volume:187 Page:108743
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Zhang Lingzhi^ORCID,Xie Lei,Su Hongye,Liu Jinfeng^ORCID

Publisher

Elsevier BV

Reference52 articles.

1. ADMMBO: Bayesian optimization with unknown constraints using ADMM;Ariafar;J. Mach. Learn. Res.: JMLR,2019

2. Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design;Bartoli;Aerosp. Sci. Technol.,2019

3. Bayesian optimization with safety constraints: Safe and automatic parameter tuning in robotics;Berkenkamp;Mach. Learn.,2021

4. Bayesian optimization with safety constraints: Safe and automatic parameter tuning in robotics;Berkenkamp;Mach. Learn.,2023

5. Safe explorative Bayesian optimization - towards personalized treatments in plasma medicine;Chan,2023

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