Applications of PSO Algorithm and OIF Elman Neural Network to Assessment and Forecasting for Atmospheric Quality

Abstract

The assessment and forecast for atmospheric quality have become the key problem in the study of the quality of atmospheric environment. In order to evaluate the grade of the atmospheric pollution, a model based on the particle swarm optimization (PSO) algorithm is proposed in this paper. Experimental results show the advantages of the proposed models, such as pellucid principle and physical explication, predigested formula and low computation complexity. In addition, an improved Elman neural network, namely, the output-input feedback Elman (OIF Elman) neural network is also applied to forecast the atmospheric quality. Simulations show that the OIF Elman neural network has great potential in the field of forecasting the atmospheric quality.