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Neural Computing and Applications

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11.07.2016 | Original Article

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

verfasst von: Yinggan Tang, Chunning Bu, Minmin Liu, LinLin Zhang, Qiusheng Lian

Erschienen in: Neural Computing and Applications | Ausgabe 2/2018

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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.

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Titel: Application of ELM–Hammerstein model to the identification of solid oxide fuel cells
verfasst von: Yinggan Tang
Chunning Bu
Minmin Liu
LinLin Zhang
Qiusheng Lian
Publikationsdatum: 11.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2453-y

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