Prediction of PM2.5 concentration based on recurrent fuzzy neural network

Abstract

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.