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A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model

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Abstract

In this paper, an online identification method is proposed for nonlinear system identification based on extreme learning machine (ELM)–Hammerstein model. The ELM–Hammerstein model comprises an ELM neural network followed by a linear dynamic subsystem. This model is linear in parameters and nonlinear in the input. To speed up the convergence and meanwhile improve identification accuracy, a changing forgetting factor recursive least squares (CFF-RLS) method is proposed as online learning algorithm. The algorithm can identify the parameters of linear dynamic subsystem and the weights of ELM neural network simultaneously. Simulation results verify the effectiveness of the proposed method.

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Acknowledgments

This work is 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, Zhenzhen Han, Ying Wang & Linlin Zhang

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

    Qiushen Lian

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  1. Yinggan Tang
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  2. Zhenzhen Han
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  3. Ying Wang
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Correspondence to Yinggan Tang.

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Tang, Y., Han, Z., Wang, Y. et al. A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model. Neural Comput & Applic 28 (Suppl 1), 813–827 (2017). https://doi.org/10.1007/s00521-016-2394-5

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  • DOI: https://doi.org/10.1007/s00521-016-2394-5

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