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

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Abstract

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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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Nos. 61273260, 61471313), Natural Science Foundation of Hebei Province (No. F2014203208), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20121333120010), China Postdoctoral Science Foundation (Nos. 2013M530888, 2014T70229).

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Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Yinggan Tang, Chunning Bu, Minmin Liu & LinLin Zhang

  2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Qiusheng Lian

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  1. Yinggan Tang
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  2. Chunning Bu
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  3. Minmin Liu
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Correspondence to Yinggan Tang.

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Tang, Y., Bu, C., Liu, M. et al. Application of ELM–Hammerstein model to the identification of solid oxide fuel cells. Neural Comput & Applic 29, 401–411 (2018). https://doi.org/10.1007/s00521-016-2453-y

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