弹性网下基于LSTM的分解炉出口温度预测

doi:10.16182/j.issn1004731x.joss.20-0012

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (5): 1078-1085.doi: 10.16182/j.issn1004731x.joss.20-0012

弹性网下基于LSTM的分解炉出口温度预测

于广宇¹, 董学平¹, 王祥民¹, 甘敏²

1.合肥工业大学电气与自动化工程学院,安徽合肥 230009;
2.福州大学数学与计算机科学学院,福建福州 350108

收稿日期:2020-01-06 修回日期:2020-02-01 出版日期:2021-05-18 发布日期:2021-06-09
作者简介:于广宇(1995-),男,硕士,研究方向为系统建模分析与控制。E-mail：whrcygy@163.com
基金资助:
国家自然科学基金(61673155)

Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM

Yu Guangyu¹, Dong Xueping¹, Wang Xiangmin¹, Gan Min²

1. School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China;
2. School of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

Received:2020-01-06 Revised:2020-02-01 Online:2021-05-18 Published:2021-06-09

摘要/Abstract

摘要： 分解炉出口温度是水泥生产过程中的关键指标。针对传统预测方法只考虑风、煤、料影响的问题,提出一种弹性网(ElasticNet)结合长短时记忆(Long Short-Term Memory,LSTM)神经网络的温度预测模型。利用弹性网方法对不同变量进行参数估计,充分考虑影响因素并实现变量筛选,同时分析隐含层和节点数对神经网络精确度的影响,构建ElasticNet-LSTM出口温度预测模型。仿真结果表明：所提出的方法优于LSTM, LASSO(Least Absolute Shrinkage and Selection Operator)-LSTM, BP神经网络和RBF神经网络方法,具有较高的预测精度。

关键词: 分解炉出口温度, 弹性网, 长短时记忆神经网络, 变量筛选

Abstract: The outlet temperature of the decomposition furnace is a key indicator in the cement production process. Aiming at the problem that traditional prediction methods only consider the influence of wind, coal, and materials, a temperature prediction model of ElasticNet combined with Long Short-Term Memory (LSTM) neural network is proposed. The ElasticNet-LSTM export temperature prediction model is constructed by using the ElasticNet method to estimate the parameters of different variables, fully considering the influencing factors and realizing the variable screening, and analyzing the influence of the number of hidden layers and nodes on the accuracy of the neural network. Simulation results show that the proposed method is superior to LSTM, Least Absolute Shrinkage and Selection Operator-LSTM, BP neural network, and RBF neural network, and has higher prediction accuracy.

Key words: decomposition furnace outlet temperature, ElasticNet, long short-term memory neural network, feature selection

中图分类号:

TP391.9

于广宇, 董学平, 王祥民, 甘敏. 弹性网下基于LSTM的分解炉出口温度预测[J]. 系统仿真学报, 2021, 33(5): 1078-1085.

Yu Guangyu, Dong Xueping, Wang Xiangmin, Gan Min. Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM[J]. Journal of System Simulation, 2021, 33(5): 1078-1085.

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弹性网下基于LSTM的分解炉出口温度预测

Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM

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