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Complex-Valued Neural Networks for Wave-Based Realization of Reservoir Computing

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Neural Information Processing (ICONIP 2017)

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

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Abstract

In this paper, we discuss the significance of complex-valued neural-network (CVNN) framework in energy-efficient neural networks, in particular in wave-based reservoir networks. Physical-wave reservoir networks are highly enhanced by CVNNs. From this viewpoint, we also compare the features of reservoir computing and other architectures.

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Notes

  1. 1.

    This paper concentrates upon a long-span perspective of reservoir networks with CVNNs. Detailed dynamics of CVNNs are given in literature such as Ref. [17].

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Author information

Authors and Affiliations

  1. Social Cooperation Program on Energy Efficient Information Processing (EEIP) and Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan

    Akira Hirose, Ryosho Nakane & Gouhei Tanaka

  2. IBM Research - Tokyo, Tokyo, Japan

    Seiji Takeda, Toshiyuki Yamane, Daiju Nakano & Shigeru Nakagawa

Authors
  1. Akira Hirose
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  2. Seiji Takeda
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  3. Toshiyuki Yamane
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  4. Daiju Nakano
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  5. Shigeru Nakagawa
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  6. Ryosho Nakane
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  7. Gouhei Tanaka
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Corresponding author

Correspondence to Akira Hirose .

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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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Hirose, A. et al. (2017). Complex-Valued Neural Networks for Wave-Based Realization of Reservoir Computing. 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_47

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

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