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Toward self‐driving processes: A deep reinforcement learning approach to control
Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …
such controllers require regular maintenance to maintain acceptable performance. It is a …
Deep reinforcement learning for process control: A primer for beginners
Advanced model-based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …
such controllers require regular maintenance to maintain acceptable performance. It is a …
[HTML][HTML] Sequential Experiment Design for Parameter Estimation of Nonlinear Systems using a Neural Network Approximator
We consider the problem of sequential parameter estimation of a nonlinear function under
the Bayesian setting. The designer can choose inputs for a sequence of experiments to …
the Bayesian setting. The designer can choose inputs for a sequence of experiments to …
Bayesian identification of non-linear state-space models: Part II-Error Analysis
In the last two decades, several methods based on sequential Monte Carlo (SMC) and
Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of …
Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of …
Process Proportional-Integral PI Control with Deep Reinforcement Learning
Advanced model-based controllers in process industries require regular maintenance to
maintain acceptable performance. Controller performance is continuously monitored and …
maintain acceptable performance. Controller performance is continuously monitored and …
A data-driven digital twin approach to optimize continuous production environment with deep reinforcement learning
AH Sivri - 2023 - open.metu.edu.tr
Today, the world mainly strives to minimize carbon emissions and maximize efficiency in
energy production to lower energy costs and greenhouse effect. Therefore, optimizing …
energy production to lower energy costs and greenhouse effect. Therefore, optimizing …
Recalculation of initial conditions for the observable canonical form of state-space representation
M Garan, I Kovalenko - Proceedings of the 5th International Conference …, 2016 - dl.acm.org
This paper provides the technique for recalculation of initial conditions for state vector in
observable canonical form of state-space representation for linear dynamical systems …
observable canonical form of state-space representation for linear dynamical systems …
Error analysis in Bayesian identification of non-linear state-space models
In the last two decades, several methods based on sequential Monte Carlo (SMC) and
Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of …
Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of …