| 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].,2018,58(3):316-323 |
| 基于改进粒子群优化BP_Adaboost神经网络的PM2.5浓度预测 |
| PM2.5 concentration prediction using BP_Adaboost neural network based on modified particle swarm optimization |
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| DOI：10.7511/dllgxb201803013 |
| 中文关键词: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 |
| 英文关键词:gray correlation analysisBP_Adaboost neural networkPM2.5 concentration prediction modelmodified particle swarm optimization (MPSO) algorithm |
| 基金项目:国家自然科学基金资助项目(6147303461673053)；北京科技新星计划交叉学科合作项目(Z161100004916041). |
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| 中文摘要: |
| 为了提高大气污染物浓度预测精度，采用灰色关联分析选取影响大气中PM2.5浓度的主要因子，并以此作为神经网络输入变量，建立一种基于BP_Adaboost神经网络的PM2.5浓度预测模型．用改进粒子群算法来选择BP_Adaboost神经网络权重和阈值，可以有效避免神经网络在训练时陷入局部最优解．根据北京市海淀区万柳监测站和朝阳区北京工业大学监测点每小时监测的大气污染物浓度和气象条件，分别选择2014-11-01~2014-11-25和2017-07-07~2017-08-06数据作为实验研究对象．仿真结果表明，在PM2.5浓度预测中，相比于BP_Adaboost、BP和广义回归神经网络3种预测模型，改进粒子群优化BP_Adaboost神经网络预测性能更优． |
| 英文摘要: |
| 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 PM2.5 concentration in the atmosphere. Regarding them as the input variables, a model based on BP_Adaboost neural network is proposed to predict the PM2.5 concentration. 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 object. The simulation results show that the PM2.5 concentration prediction performance of MPSO-BP_Adaboost neural network is better than that of BP_Adaboost, BP and generalized regression neural network. |
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