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- Title
基于学习型人工蜂群算法优化双向GRU的乙烯产率预测.
- Authors
温在鑫; 钱 斌; 胡 蓉; 金怀平; 杨媛媛
- Abstract
Aiming at prediction problem that takes ethylene yield as production index, this paper establishes the ethylene yield prediction model based on the bi-directional gated recurrent neural network (BGRU), a learning based artificial bee colony algorithm (LABC) is proposed to optimize and design the prediction model with the goal of minimizing model error. When constructing the BGRU prediction model, the actual production process of ethylene cracking furnace is analyzed to determine the key factors that affect the yield and take them as the input of the model. In addition, LABC is designed to comprehensively evolve and design the structure, initial weight and threshold, training ratio and momentum factor of the BGRU model. In LABC, the state set, action set, reward function and optimal hybrid search strategy in reinforcement learning framework are constructed according to the characteristics of artificial bee colony algorithm (ABC), on this basis, a new deep double Q network (DDQN) is proposed to realize the optimal hybrid search strategy. Through this strategy, appropriate search actions can be intelligently selected to perform local search for different states. Results of experiments and comparisons on actual production data and standard data set demonstrate that LABC BGRU model has the characteristics of high prediction accuracy and strong applicability.
- Subjects
DEEP reinforcement learning; REINFORCEMENT learning; INDUSTRIAL capacity; FURNACES; ETHYLENE
- Publication
Control Theory & Applications / Kongzhi Lilun Yu Yinyong, 2023, Vol 40, Issue 10, p1746
- ISSN
1000-8152
- Publication type
Article
- DOI
10.7641/CTA.2022.20211