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International Conference on Neural Information Processing

ICONIP 2021: Neural Information Processing pp 262–272Cite as

Performance Improvement of FORCE Learning for Chaotic Echo State Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13109))

Abstract

Echo state network (ESN) is a kind of recurrent neural networks (RNNs) which emphasizes randomly generating large-scale and sparsely connected RNNs coined reservoirs and only training readout weights. First-order reduced and controlled error (FORCE) learning is an effective online training approach for chaotic RNNs. This paper proposes a composite FORCE learning approach enhanced by memory regressor extension to train chaotic ESNs efficiently. In the proposed approach, a generalized prediction error is obtained by using regressor extension and linear filtering operators with memory to retain past excitation information, and the generalized prediction error is applied as additional feedback to update readout weights such that partial parameter convergence can be achieved rapidly even under weak partial excitation. Simulation results based on a dynamics modeling problem indicate that the proposed approach largely improves parameter converging speed and parameter trajectory smoothness compared with the original FORCE learning.

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Acknowledgments

This work was supported in part by the Guangdong Pearl River Talent Program of China under Grant No. 2019QN01X154.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Ruihong Wu & Yongping Pan

  2. Graduate School of Information Science and Technology, University of Tokyo, Tokyo, 113-8656, Japan

    Kohei Nakajima

Authors
  1. Ruihong Wu
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  2. Kohei Nakajima
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  3. Yongping Pan
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Corresponding author

Correspondence to Yongping Pan .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Wu, R., Nakajima, K., Pan, Y. (2021). Performance Improvement of FORCE Learning for Chaotic Echo State Networks. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_23

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  • DOI: https://doi.org/10.1007/978-3-030-92270-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92269-6

  • Online ISBN: 978-3-030-92270-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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