Prediction of PM2.5 based on Elman neural network with chaos theory

被引：0
|
作者：
Hu Zhiqiang [1 ,2 ]
Li Wenjing [1 ,2 ]
Qiao Junfei [1 ,2 ]
机构：
[1] Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
[2] Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
来源：
PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年
基金：
美国国家科学基金会; 中国博士后科学基金;
关键词：
PM2.5; Chaos; Phase space reconstruction; Elman neural network; Prediction; TIME-SERIES PREDICTION; IDENTIFICATION; PRICE; MODEL; PM10;
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：
PM2.5 is difficult to accurately forecast due to the influence of multiple meteorological and pollutant variables in the complex nonlinear dynamic atmosphere system. In this paper, an Elman neural network prediction method based on chaos theory is put forward for the problem. Firstly, the chaotic characteristics of the concentration of the PM2.5 are analyzed and verified from the correlation dimension, the maximum Lyapunov exponent and the Kolmogorov entropy. Then, phase space reconstruction technique of chaotic theory is adopted to reconstruct the phase space of PM2.5 time series. The reconstructed phase space and the future concentration of PM2.5 are taken as the input and output of the Elman neural network with chaos theory (Elman-chaos) respectively. The numerical and experimental analyses show that this method is proportionally superior to that without considering the chaos characteristics and other approaches. The Elman-chaos prediction model has better prediction performance and application value.
引用
收藏
页码：3573 / 3578
页数：6
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