A Forecasting Model of RBF Neural Network Based on Particle Swarm Optimization

摘要

In order to improve the precision of gas emission forecasting, this paper proposes a new forecasting model based on Particle Swarm Optimization (PSO). PSO is a novel random optimization method which has extensive capability of global optimization. In the model, PSO is used to optimize the weight, width and center of RBF neural network and the optimal model is applied to forecast gas emission. The diversified factors analysised with grey correlation, MATLAB is employed to implement the model for gas emission forecasting. The simulation results show that the gas emission model optimized by PSO is more accurate than the traditional RBF model.