Modeling of superheated steam temperature system based on improved pruning algorithm

Based on the method of Skeletonization, the concept of influence factor is introduced in this paper. A method for trimming the fat from a Back Propagation (BP) neural network is proposed by modifying weight and influence factor alternately, and node with the least influence factor was deleted. This method is applied to modeling superheated steam temperature system of plant station. Simulation results show that this pruning algorithm meets the demand of precision with higher convergence rate, and the generalization ability greatly improves.