摘要
为了提高空气质量预测精度,提出一种基于粒子群算法优化极限学习机的空气质量预测模型.运用粒子群算法优化极限学习机的初始权值和偏置,在保证预测误差最小的情况下实现空气质量最优预测.选择平均绝对百分比误差、均方根误差和平均绝对误差作为评价指标,通过PSO-ELM、GA-ELM、SOA-ELM、DE-ELM和ELM五个模型预测结果对比发现,PSO-ELM可以有效提高空气质量预报的预测精度,可为空气质量预测提供新的方法和途径.
In order to improve the accuracy of air quality prediction,an air quality prediction model based on the particle swarm algorithm optimization for extreme learning machine( PSO-ELM) was proposed. The initial weight and bias of extreme learning machine were optimized with the particle swarm,and the optimal air quality prediction was achieved when the prediction error was minimized. By choosing mean absolute percentage,root mean square and mean absolute errors as evaluation criteria and comparing the results of five models,i.e. PSO-ELM,GA-ELM,SOA-ELM,DE-ELM and ELM,it was discovered that PSO-ELM can effectively improve the accuracy of air quality prediction and pave new ways and tracks for air quality prediction.
作者
庄玉册
黎蔚
ZHUANG Yu-ce;LI Wei(School of Mathematics and Information,Xinyang University,Xinyang 464000,China;School of Information Engineering,Henan University of Science and Technology,Luoyang 471003,China)
出处
《沈阳工业大学学报》
EI
CAS
北大核心
2020年第2期213-217,共5页
Journal of Shenyang University of Technology
基金
河南省科技重点攻关计划项目(102102210419).
关键词
粒子群算法
极限学习机
空气质量指数
神经网络
相对误差
遗传算法
差分进化算法
人群搜索算法
particle swarm algorithm
extreme learning machine
air quality index
neural network
relative error
genetic algorithm
differential evolution algorithm
seeker optimization algorithm
