Identification of CSTR using extreme learning machine based hammerstein-wiener model
作者： Zhenzhen Han , Bin Cheng , Cheng Wang , Wenhuan Yang
DOI: 10.1109/CCSSE.2017.8088031
关键词: Artificial neural network 、 Identification (information) 、 Continuous stirred-tank reactor 、 Computer science 、 Least mean square algorithm 、 Computation complexity 、 Nonlinear system 、 Algorithm 、 Extreme learning machine 、 Model parameters 、 Control engineering
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. the proposed H-W model, two blocks are described by different neural networks. The parameters identification achieve generalized least square algorithm. propose method can obtain more accurate results with less computation complexity. simulation result shows that approach effective.
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