A rough set based PSO-BPNN model for air pollution forecasting

Based on rough set theory, a multilayer back propagation neural network (BPNN) whose parameters will be trained and optimized by particle swarm optimization (PSO) is presented here. Making use of the intelligence of RS in knowledge acquisition aspect, this method carries out a pretreatment on the BPNN data, extracts the regulation from large amount of original data, predigests the nerve basics in neural networks, facilitate the neural networks structure, then employ PSO to the weight parameter and finally improve systematic speed and forecasting accuracy. After data pretreatment and attribute reduction by employing RS theory, the noise data and weak interdependency term are eliminated, so the influences during the initialization, study and training process are avoided, and then the weight parameters of each nerve cell have been optimized through PSO, as a result the accuracy of predictions is developed and proved by the evidence of forecasting with time series from the concentration of air pollutant.