基于PSO算法優(yōu)化BP神經(jīng)網(wǎng)的PM2.5濃度預(yù)測模型

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關(guān)鍵詞： 濃度；預(yù)測模型；PSO算法；BP神經(jīng)網(wǎng)絡(luò)

中圖分類號：TP391.4；TP183 文獻標識碼：A 文章編號：2096-4706（2025）07-0047-06

Abstract：Aimingat the problem thatthe traditional BPNeural Network has slowconvergence speedand iseasyto fall into local optimal solution， this paper proposes a concentration prediction model based on Particle Swarm Optimization （PSO）algorithmoptimizedBPNeuralNetwork，whichcanquicklyconvergeandgettheglobal optimalsolution.Firstly，the pollutant indexes with high correlation with concentration are selected as input variables by Pearson correlation analysis. Secondly，thePSOalgorithmisusedtooptimizetheinitialweightsandthresholdsofBPNeuralNetwork，whichovercomesthe shortcomingsofBPNeuralNetwork，suchaseasytofallintolocaloptimumandslowconvergencespeed.Finally，themodel is trained and tested using air pollutant data from July 2O21 to June 2024 in Chengdu.Theresults show that the of the test setis 0.944，theMAEofthetestsetis4.231，andtheRMSEofthetestsetis6.364.ComparedwiththeunoptimizedBPNeural Network model，thePO-BPmodelhashgherpredictionaccuracyandfasterconvergencespeed，andcaneffctivelyprdictthe concentration of the next day in Chengdu.

Keywords： concentration，prediction model，PsO algorithm，BPNeural Network

0 引言

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