智能系统学报2018，Vol.13Issue(4)：509-516,8.DOI:10.11992/tis.201710007
基于自组织递归模糊神经网络的PM2.5浓度预测
Prediction of PM2.5 concentration based on self-organizing recurrent fuzzy neural network
摘要
Abstract
To address the nonlinear dynamic variation in the concentration of fine particulate matter(PM2.5),in this pa-per,we propose a novel self-organizing recurrent fuzzy neural network(SORFNN)for predicting the hourly PM2.5 con-centration.First,we analyzed the factors affecting PM2.5 concentration by principal component analysis to identify the characteristic variables and used them as input variables in the neural network.Next,we added or deleted a nerve cell to the regularized layer,based on the ε criterion and partial least squares algorithm,to automatically adjust the recurrent fuzzy neural network.In addition,we applied the adaptive gradient descent algorithm to adjust parameters such as the centers,widths and weights to establish a PM2.5 model.Lastly,to verify the results,we conducted experiments in typic-al nonlinear system identification and actual PM2.5 concentration prediction.The experimental results show that the proposed SORFNN is compact in structure,has high prediction accuracy,and can satisfy the real-time prediction re-quirements of PM2.5 concentration.关键词
PM2.5/预测/PCA/递归模糊神经网络/自组织/自适应梯度下降Key words
PM2.5/prediction/PCA/recurrent fuzzy neural network/self-organizing/adaptive gradient descent al-gorithm分类
信息技术与安全科学引用本文复制引用
周杉杉,李文静,乔俊飞..基于自组织递归模糊神经网络的PM2.5浓度预测[J].智能系统学报,2018,13(4):509-516,8.基金项目
国家自然科学基金项目(61533002,61603009) （61533002,61603009）
北京工业大学"日新人才"计划项目(2017-RX(1)-04) （2017-RX(1）
北京市自然科学基金项目(4182007). （4182007）
