Prediction of PM2.5 Concentration Based on Recurrent Fuzzy Neural Network
作者单位：Faculty of Information TechnologyBeijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System
会议名称：《第36届中国控制会议》
主办单位：Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
会议日期：2017年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Science Foundation of China under Grants 61225016,61533002,and 61603009 the China Postdoctoral Research Foundation under Grant 2015M570910 the Chao Yang District Postdoctoral Research Foundation under Grant 2015ZZ-6 the Basic Research Foundation Project of Beijing University of Technology under Grant 002000514315501
关 键 词：PM2.5 prediction recurrent fuzzy neural network PLS adaptive learning rate
摘 要：The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic ***,a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this ***,the partial least squares（PLS） algorithm is used to select key input variables as a preprocessing ***,a recurrent fuzzy neural network model is established and the gradient descent algorithm with an adaptive learning rate is used to train the neural *** 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.