Mar 7, 2020 · This paper proposes a model-free Deep Deterministic Policy Gradient (DDPG) learning controller for zinc electrowinning processes (ZEP) to ...
This paper proposes a model-free Deep Deterministic Policy Gradient (DDPG) learning controller for zinc electrowinning processes (ZEP) to save energy ...
A two-stage DPG-based RL optimal control algorithm is proposed, in which a novel finite-horizon performance index is employed in the pre-learning stage such ...
“Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning” is a paper by Xiaotian Shi Yonggang Li Bin ...
This paper is concerned with the optimal control problem for the zinc electrowinning process during the current switching period.
A novel time-scaling transformation-based control parametrization method is introduced to transform the optimal control problem into a multiple parameters ...
Feb 21, 2024 · 李勇刚,liyonggang,中南大学, Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning
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Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning. ... Machine learning and deep learning methods ...
Jan 23, 2023 · Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning. Neurocomputing 380 190–200 ...