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Models of Self-correlation Type Complex-Valued Associative Memories and Their Dynamics

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Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

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Abstract

Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. We have already proposed a model of self-correlation type associative memories using complex-valued neural networks with one of the most commonly used activation function. In this paper, we propose two additional models using different nonlinear complex functions and investigated their behaviors as associative memories theoretically. Comparisons are also made among these three models in terms of dynamics and storage capabilities.

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Author information

Authors and Affiliations

  1. Center for Information Science,  

    Yasuaki Kuroe

  2. Department of Electronics and Information Science, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto, 606-8585, Japan

    Yuriko Taniguchi

Authors
  1. Yasuaki Kuroe
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  2. Yuriko Taniguchi
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Kuroe, Y., Taniguchi, Y. (2005). Models of Self-correlation Type Complex-Valued Associative Memories and Their Dynamics. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_30

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  • DOI: https://doi.org/10.1007/11550822_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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