Identification of CSTR using extreme learning machine based hammerstein-wiener model

摘要

In this paper, an extreme learning machine based Hammerstein-Wiener(H-W) model is built to identify continuous Stirred Tank Reactor(CSTR) nonlinear system. In the proposed H-W model, the two nonlinear blocks are described by two different extreme learning machine neural networks. The model parameters identification is achieve by generalized least square algorithm. The propose method can obtain more accurate identification results with less computation complexity. The simulation result shows that this proposed approach is effective.