摘要
为了更好地捕捉瓦斯浓度的时变规律及有效信息,实现对采煤工作面瓦斯浓度的精准预测,采用麻雀搜索算法(SSA)优化长短期记忆(LSTM)网络,提出了一种基于SSA-LSTM的瓦斯浓度预测模型。采用均值替换法对原始瓦斯浓度时序数据中的缺失数据及异常数据进行处理,再进行归一化和小波阈值降噪;对比测试了SSA与灰狼优化(GWO)算法、粒子群优化(PSO)算法的性能差异,验证了SSA在寻优精度、收敛速度和适应能力等方面的优势;利用SSA的自适应性依次对LSTM的学习率、隐藏层节点个数、正则化参数等超参数进行寻优,以此来提高全局寻优能力,避免预测模型陷入局部最优;将得到的最佳超参数组合代入LSTM网络模型中,输出预测结果。将SSA-LSTM与LSTM、GWO-LSTM、PSO-LSTM瓦斯浓度预测模型进行比较,实验结果表明:基于SSA-LSTM的瓦斯浓度预测模型的均方根误差(RMSE)较LSTM,PSO-LSTM,GWO-LSTM分别减少了77.8%,58.9%,69.7%;平均绝对误差(MAE)分别减少了83.9%,37.8%,70%,采用SSA优化的LSTM预测模型相较于传统LSTM模型具有更高的预测精度和鲁棒性。
In order to better capture the time-varying patterns and effective information of gas concentration,and achieve precise prediction of gas concentration in coal working faces,a gas concentration prediction model based on SSA-LSTM is proposed by optimizing the long short term memory(LSTM)network using sparrow search algorithm(SSA).The model uses the mean replacement method to process missing and abnormal data in the original gas concentration time series data,followed by normalization and wavelet threshold denoising.The performance differences between SSA and grey wolf optimization(GWO)and particle swarm optimization(PSO)algorithms are compared and tested.The result verifies the advantages of SSA in terms of optimization precision,convergence speed,and adaptability.By utilizing the adaptability of SSA,the hyperparameters of LSTM,such as learning rate,number of hidden layer nodes,and regularization parameters,are sequentially optimized to improve the global optimization capability and avoid the prediction model falling into local optimum.The obtained optimal hyperparameter combination is substituted into the LSTM network model and the prediction results are output.Comparing SSA-LSTM with LSTM,GWO-LSTM,and PSO-LSTM gas concentration prediction models,the experimental results show that the root mean square error(RMSE)of the gas concentration prediction model based on SSA-LSTM is reduced by 77.8%,58.9%,and 69.7%compared to LSTM,PSO-LSTM,and GWOLSTM,respectively.The mean absolute error(MAE)decreases by 83.9%,37.8%,and 70%,respectively.The LSTM prediction model optimized by SSA has higher prediction precision and robustness compared to traditional LSTM models.
作者
兰永青
乔元栋
程虹铭
雷利兴
罗化峰
LAN Yongqing;QIAO Yuandong;CHENG Hongming;LEI Lixing;LUO Huafeng(School of Coal Engineering,Shanxi Datong University,Datong 037003,China;School of Architecture and Geomatics Engineering,Shanxi Datong University,Datong 037003,China)
出处
《工矿自动化》
CSCD
北大核心
2024年第2期90-97,共8页
Journal Of Mine Automation
基金
山西省回国留学人员科研资助项目(2022174)
山西省高校科技创新项目(2021L397)。
关键词
瓦斯浓度预测
时序预测
深度学习
长短期记忆网络
麻雀搜索算法
超参数寻优
gas concentration prediction
time series prediction
deep learning
long short-term memory network
sparrow search algorithm
hyperparameter optimization
