Boiler combustion efficiency optimization based on improved radial basis neural network

Abstract

In order to improve the training accuracy of radial basis neural network,this paper proposed a hybrid optimization algorithm.The algorithm used the strong global search ability of Particle Swarm Optimization(PSO) algorithm to avoid the adverse effect by choosing initial point in the K-means algorithm,thus improving the network center search speed.Meanwhile,the dynamic weight algorithm was used to avoid the ill-posed problem,and to further improve the network approximation ability.The boiler combustion instance indicates that the improved algorithm is efficient and practical.