Improved GA-RBF Neural Network for Predicting Coagulant Dosing in Waterworks
Aiming to improve the accuracy of prediction of coagulant dosage in waterworks,with the dosing process had highly nonlinear and influence about a variety of water quality factors.The weight ωi and the central width vector σi of the basis function of the Radial Basis Function(RBF)neural network are optimized by improving Genetic Algorithm(GA),construction of GA-RBF neural network prediction model for chemical dosage of waterworks.The simulation results by Matlab showed that the GA-RBF neural network prediction model could avoid the extreme value trap by implementing global approximation,and improved the stability and global optimization ability.Compared with the single RBF neural network prediction model,the R2 of the GA-RBF neural network prediction model increased by 5.474%,the MAE decreased by 4.14%,and the RMSE decreased by 3.392%.The iteration speed and prediction accuracy were improved,and the data fitting ability was stronger.
coagulant dosagedosing systemgenetic algorithmRBF neural networkprediction model
万方数据
维普
