Prediction of Blast Furnace Gas Output Based on GA-Elman Neural Network

被引：1
|
作者：
Zhu, Yong [1 ]
Ma, Ruijie [1 ]
Wu, Dinghui [1 ]
Shen, Yanxia [1 ]
机构：
[1] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年
基金：
中国国家自然科学基金;
关键词：
Local Optimum; GA-Elman; BFG Output; Prediction Model; Working Conditions;
D O I：
10.1109/CCDC52312.2021.9602694
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Blast furnace gas (BFG) output in steel industry fluctuate sharply and there are many influencing factors. It is difficult to establish a suitable mechanism model to predict it effectively, and the prediction accuracy of a single neural network model is low. In order to improve the accuracy, an Elman neural network (ENN) prediction model based on Genetic algorithm(GA) is proposed. By optimizing the initial weights and thresholds of the ENN, the possibility of network falling into local optimum is reduced greatly. Combining the characteristics of BFG output with actual data to simulate and analyze, the simulation results show that compared with other models, the model proposed in this paper has higher accuracy.
引用
收藏
页码：1337 / 1342
页数：6
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