Prediction of PM2.5 Concentration Based on Recurrent Fuzzy Neural Network

被引：0
|
作者：
Zhou, Shanshan [1 ,2 ]
Li, Wenjing [1 ,2 ]
Qiao, Junfei [1 ,2 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
基金：
美国国家科学基金会;
关键词：
PM2.5; prediction; recurrent fuzzy neural network; PLS; adaptive learning rate; PARTICULATE MATTER PM2.5; PM10; MODEL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic process. Therefore, a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this paper. Firstly, the partial least squares (PLS) algorithm is used to select key input variables as a preprocessing step. Then, a recurrent fuzzy neural network model is established and the gradient descent algorithm with an adaptive learning rate is used to train the neural network. Simulation results show that the recurrent neural network has better prediction performance and higher interpretability than fuzzy neural network (FNN) and radial-basis function (RBF) feed forward neural network.
引用
收藏
页码：3920 / 3924
页数：5
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