Combing with the hot state test data of an ultra supercritical 660 MW coal-fired boiler

the radial basis function(RBF)neural network and BP neural network regression algorithms were used to establish models for NOxemissions from this boiler

by using the intelligent MATLAB toolbox.Moreover

optimization was performed for the RBF neural network prediction model of output layer weights and the center of RBF function and standard deviation

and the momentum method was adopted to improve the problems existing in the BP neural network.The results show that

the maximum error of the RBF prediction model after parameter optimization simulation was 3.0% and the average error was 1.75%.The maximum error of the improved BP network prediction model was 6.6%and the average error was 4.5%.Both the above two modeling methods have well accuracy and generalization ability.The RBF neural network model is obviously superior to the BP neural network model in terms of computing speed

fitting ability and generalization ability.