现代信息科技2025，Vol.9Issue(7)：47-51,57,6.DOI:10.19850/j.cnki.2096-4706.2025.07.009
基于PSO算法优化BP神经网络的PM2.5浓度预测模型
PM2.5 Concentration Prediction Model Based on PSO Algorithm Optimized BP Neural Network
摘要
Abstract
Aiming at the problem that the traditional BP Neural Network has slow convergence speed and is easy to fall into local optimal solution,this paper proposes a PM2.5 concentration prediction model based on Particle Swarm Optimization(PSO)algorithm optimized BP Neural Network,which can quickly converge and get the global optimal solution.Firstly,the pollutant indexes with high correlation with PM2.5 concentration are selected as input variables by Pearson correlation analysis.Secondly,the PSO algorithm is used to optimize the initial weights and thresholds of BP Neural Network,which overcomes the shortcomings of BP Neural Network,such as easy to fall into local optimum and slow convergence speed.Finally,the model is trained and tested using air pollutant data from July 2021 to June 2024 in Chengdu.The results show that the R2 of the test set is 0.944,the MAE of the test set is 4.231,and the RMSE of the test set is 6.364.Compared with the unoptimized BP Neural Network model,the PSO-BP model has higher prediction accuracy and faster convergence speed,and can effectively predict the PM2.5 concentration of the next day in Chengdu.关键词
PM2.5浓度/预测模型/PSO算法/BP神经网络Key words
PM2.5 concentration/prediction model/PSO algorithm/BP Neural Network分类
信息技术与安全科学引用本文复制引用
李佳林,侯利明,张聪..基于PSO算法优化BP神经网络的PM2.5浓度预测模型[J].现代信息科技,2025,9(7):47-51,57,6.基金项目
四川卫生康复职业学院重点课题(CWKY-2019Z-02) （CWKY-2019Z-02）
四川卫生康复职业学院校级科研团队(CWKY-TD24-10) （CWKY-TD24-10）
