Application of ELM-Hammerstein model to the identification of solid oxide fuel cells

作者：Tang, Yinggan^*; Bu, Chunning; Liu, Minmin; Zhang, LinLin; Lian, Qiusheng

来源：Neural Computing & Applications, 2018, 29(2): 401-411.

DOI：10.1007/s00521-016-2453-y

• Solid oxide fuel cell
• Identification
• Extreme learning machine
• Hammerstein model

In this paper, a new method is proposed to identify solid oxide fuel cell using extreme learning machine-Hammerstein model (ELM-Hammerstein). The ELM-Hammerstein model consists of a static ELM neural network followed by a linear dynamic subsystem. First, the structure of ELM-Hammerstein model is determined by Lipschitz quotient criterion from input-output data. Then, a generalized ELM algorithm is proposed to estimate the parameters of ELM-Hammerstein model, including the parameters of linear dynamic part and the output weights of ELM. The proposed method can obtain accurate identification results and its computation is more efficient. Simulation results demonstrate its effectiveness.

• 出版日期2018-1
• 单位中国科学院电工研究所; 燕山大学