An Atmospheric Quality Forecasting Model Based on RBF Neural Network and Its Applications

Because of the strong nonlinear characteristics of the variation of the atmospheric pollution, a more powerful tool is needed to illustrate the nonlinear phenomenon. The forward neural network of Radial Basis Function (RBF) neural network is used to forecast atmospheric quality in this paper, which has many advantages such as simple structure, laconic training, rapid convergence, etc. Simulation and numeric results show that the model is effcctive and feasible, it has great potential in the field of forecasting the atmospheric quality. Therefore it could provide a new conference tool and approach for renovating and layout of atmospheric environment.