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Proposal of Online Regularization for Dynamical Structure Optimization in Complex-Valued Neural Networks

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Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11954))

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Abstract

We propose online-learning complex-valued neural networks (CVNN) to predict future channel states in fast fading multipath mobile communications. A communication channel is represented by complex amplitude. Then, CVNNs are intrinsically suitable for fading channel prediction by utilizing its high generalization ability in the complex-amplitude domain. In this paper, we introduce regularization dynamics to make the CVNN structure dynamically changing in accordance with the changes in the multipath situations. We demonstrate the online adaptability when the scattering environment changes.

A. Hirose—A part of this work was supported by JSPS KAKANHI under Grant 15H02756 and Grant 18H04105, and a part by Tohoku University RIEC Cooperative Research Project.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, 153-8656, Japan

    Tianben Ding & Akira Hirose

  2. Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA

    Tianben Ding

Authors
  1. Tianben Ding
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  2. Akira Hirose
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Correspondence to Akira Hirose .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

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

    Minho Lee

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Ding, T., Hirose, A. (2019). Proposal of Online Regularization for Dynamical Structure Optimization in Complex-Valued Neural Networks. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_34

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  • DOI: https://doi.org/10.1007/978-3-030-36711-4_34

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  • Online ISBN: 978-3-030-36711-4

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