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Growing Greedy Search and Its Application to Hysteresis Neural Networks

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Book cover Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

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Abstract

This paper presents the growing greedy search algorithm and its application to associative memories of hysteresis neural networks in which storage of desired memories are guaranteed. In the algorithm, individuals correspond to cross-connection parameters, the cost function evaluates the number of spurious memories, and the set of individuals can grow depending on the global best. Performing basic numerical experiments, the algorithm efficiency is investigated.

T. Saito—This work is supported in part by JSPS KAKENHI\(\#\)15K00350.

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Authors and Affiliations

  1. Hosei University, Koganei, Tokyo, 184-8584, Japan

    Kei Yamaoka & Toshimichi Saito

Authors
  1. Kei Yamaoka
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  2. Toshimichi Saito
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Corresponding author

Correspondence to Toshimichi Saito .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Yamaoka, K., Saito, T. (2015). Growing Greedy Search and Its Application to Hysteresis Neural Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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