Modeling of Superheated Steam Temperature using Sparse Least Squares Support Vector Networks

Abstract

Super-heater steam temperature in power plant is the strong nonlinearity system. Though neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the "best" structure of the neural network is more difficulty. Sparse least squares support vector networks (SLSVN) are proposed to model the superheated steam of power plant in this paper. The structure of the SLSVN is obtained by equality-constrained minimization. Under the condition of modeling approximating to performance, the pruning algorithm gets the sparse modeling. The merits of the algorithm are conforming to the least structural risk in training process and hardly leading to over-fitting. The simulation of a superheating system, in a 600 MW supercritical concurrent boiler, is taken. The result shows that the proposed SLSVN model can adapt to the strong nonlinear super-heater steam temperature process