重庆理工大学学报2025，Vol.39Issue(9)：29-35,7.DOI:10.3969/j.issn.1674-8425(z).2025.05.004
融合海鸥算法及LSTM的燃料电池城市客车车速预测研究
Research on speed prediction of fuel cell city bus based on ISOA-LSTM
摘要
Abstract
To address the issue of low prediction accuracy in fuel cell city buses speed forecasting,a speed prediction model combining improved Seagull Optimization Algorithm(ISOA)and Long Short-Term Memory neural network(LSTM)is proposed.The standard driving cycle database is used as the training set,while the China typical city bus driving cycle serves as the test set.The seagull optimization algorithm improved by introducing Levy flight,Cauchy mutation and other strategies is employed to determine the optimal parameters of the LSTM.An ISOA-LSTM fuel cell city bus speed prediction model was established,and compared with LSTM model,SOA-LSTM model and GWO-LSTM model.Experimental results demonstrate that the proposed ISOA-LSTMmodel achieves superior prediction accuracy,with a root mean square error(RMSE)of 1.965,mean absolute error(MAE)of 1.570,and coefficient of determination(R2)of 0.983.关键词
燃料电池城市客车/车速预测/改进海鸥优化算法/LSTM神经网络Key words
fuel cell city buses/vehicle speed prediction/ISOA/LSTMneural network分类
信息技术与安全科学引用本文复制引用
何锋,陈鹏,刘勇,边东生,龚成平..融合海鸥算法及LSTM的燃料电池城市客车车速预测研究[J].重庆理工大学学报,2025,39(9):29-35,7.基金项目
贵州省科技计划项目(黔科合支撑[2023]一般 400) （黔科合支撑[2023]一般 400）
贵州省科技计划项目(黔科合支撑[2024]一般 069) （黔科合支撑[2024]一般 069）
