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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 243–252Cite as

Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

Abstract

This paper presents the deduction of the scaled conjugate gradient method for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of the scaled conjugate algorithm in the real- and complex-valued cases lead to the idea of extending it to the quaternion domain, also. Experiments done using the proposed training method on time series prediction applications showed a significant performance improvement over the quaternion gradient descent and quaternion conjugate gradient algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer and Software Engineering, Polytechnic University Timişoara, Blvd. V. Pârvan, No. 2, 300223, Timişoara, Romania

    Călin-Adrian Popa

Authors
  1. Călin-Adrian Popa
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Correspondence to Călin-Adrian Popa .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

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

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Popa, CA. (2016). Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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