计算机应用研究2017，Vol.34Issue(2)：405-408,4.DOI:10.3969/j.issn.1001-3695.2017.02.019
加权因子的PSO-SVR区域空气PM2.5浓度预报方法
Regional PM2.5 concentration prediction method of PSO-SVR model with weighting factors
摘要
Abstract
This paper developed a regional air PM2.5 concentration predicting model with weighting factors (W-PSO-SVR),which combined support vector regression(SVR) and particle swarm optimization (PSO).The [0,1] unequal weighting factors which were achieved by the PSO search were assigned to the input variables of the model.When the unequal weighting factors were confirmed,then it established the PM2.5 predicting model.Compared with the pure SVR model and 0 or I weighting factors' SVR model,predicting results indicate that W-PSO-SVR model performs better and the predicting accuracy is higher.Besides,the W-PSO-SVR model can achieve the better effective selection of input parameters.关键词
PM2.5预报/支持向量机/粒子群优化算法/加权因子Key words
PM2.5 predicting/support vector machine/particle swarm optimization/weighting factor分类
信息技术与安全科学引用本文复制引用
杨忠,童楚东,俞杰,傅晓钦,汪伟峰,史旭华..加权因子的PSO-SVR区域空气PM2.5浓度预报方法[J].计算机应用研究,2017,34(2):405-408,4.基金项目
浙江省科技厅公益技术应用研究资助项目(2015C31017) （2015C31017）
浙江省自然科学基金资助项目(LY14F030004) （LY14F030004）
