工矿自动化2024，Vol.50Issue(2)：90-97,8.DOI:10.13272/j.issn.1671-251x.2023090067
基于SSA-LSTM的瓦斯浓度预测模型
Gas concentration prediction model based on SSA-LSTM
摘要
Abstract
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 GWO-LSTM,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.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Key words
gas concentration prediction/time series prediction/deep learning/long short-term memory network/sparrow search algorithm/hyperparameter optimization分类
矿业与冶金引用本文复制引用
兰永青,乔元栋,程虹铭,雷利兴,罗化峰..基于SSA-LSTM的瓦斯浓度预测模型[J].工矿自动化,2024,50(2):90-97,8.基金项目
山西省回国留学人员科研资助项目(2022174) （2022174）
山西省高校科技创新项目(2021L397). （2021L397）
