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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 423–431Cite as

An Iterative Incremental Learning Algorithm for Complex-Valued Hopfield Associative Memory

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

Abstract

This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iterations for the learning sequence is defined with maintaining the network stability. From the result of simulation experiment in terms of memory capacity and noise tolerance, the proposed model has the superior ability than the model with a complex-valued pseudo inverse learning algorithm.

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Acknowledgments

The authors would like to acknowledge a scholarship provided by the University of Malaya (Fellowship Scheme). This research is supported by High Impact Research UM.C/625/1/HIR/MOHE/FCSIT/10 from University of Malaya.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Naoki Masuyama & Chu Kiong Loo

Authors
  1. Naoki Masuyama
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  2. Chu Kiong Loo
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Corresponding author

Correspondence to Naoki Masuyama .

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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© 2016 Springer International Publishing AG

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Masuyama, N., Loo, C.K. (2016). An Iterative Incremental Learning Algorithm for Complex-Valued Hopfield Associative Memory. 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 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_51

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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