大连理工大学学报2018，Vol.58Issue(3)：316-323,8.DOI:10.7511/dllgxb201803013
基于改进粒子群优化BP_Adaboost神经网络的PM2.5浓度预测
PM2.5concentration prediction using BP_Adaboost neural network based on modified particle swarm optimization
摘要
Abstract
In order to improve the prediction accuracy of the atmospheric pollutants concentration, the gray correlation analysis is used to select the main factors affecting the PM2.5concentration in the atmosphere.Regarding them as the input variables,a model based on BP_Adaboost neural network is proposed to predict the PM2.5concentration.The modified particle swarm optimization (MPSO) algorithm is applied to choose the weight and threshold of BP_Adaboost neural network,which can availably avoid falling into local optimal solution.According to the concentration of air pollutants and meteorological condition,the data between November 1,2014 to November 25,2014 and July 7,2017 to August 6,2017,which are monitored every hour by the Wanliu station of Haidian distinct and Beijing University of Technology point of Chaoyang distinct in Beijing,are used as the experiment obj ect.The simulation results show that the PM2.5concentration prediction performance of MPSO-BP_Adaboost neural network is better than that of BP_Adaboost,BP and generalized regression neural network.关键词
灰色关联分析/BP_Adaboost神经网络/PM2.5浓度预测模型/改进粒子群算法Key words
gray correlation analysis/BP_Adaboost neural network/PM2.5concentration prediction model/modified particle swarm optimization (MPSO)algorithm分类
资源环境引用本文复制引用
李晓理,梅建想,张山..基于改进粒子群优化BP_Adaboost神经网络的PM2.5浓度预测[J].大连理工大学学报,2018,58(3):316-323,8.基金项目
国家自然科学基金资助项目(61473034,61673053) （61473034,61673053）
北京科技新星计划交叉学科合作项目(Z161100004916041). （Z161100004916041）
