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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 231–238Cite as

Parameter Identification for a Class of Nonlinear Systems Based on ESN

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

Abstract

In this paper, a new identification method based on echo state network (ESN) is proposed to identify the parameters of a class of discrete-time nonlinear systems. Through analyzing the characteristics of output signals, the identification method can determine the maximal delay time of the given nonlinear system. To obtain the better prediction and identification accuracy of this method, an online learning algorithm is proposed to train the output weights of ESN. Simulation examples show the effectiveness of the proposed identification method.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61473070, 61433004, 61627809), the Fundamental Research Funds for the Central Universities (Grant No. N160406002), and SAPI Fundamental Research Funds (Grant No. 2013ZCX01).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Xianshuang Yao, Zhanshan Wang & Huaguang Zhang

Authors
  1. Xianshuang Yao
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  2. Zhanshan Wang
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  3. Huaguang Zhang
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Corresponding author

Correspondence to Zhanshan Wang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Yao, X., Wang, Z., Zhang, H. (2017). Parameter Identification for a Class of Nonlinear Systems Based on ESN. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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