Dynamics of Discrete-Time Quaternionic Hopfield Neural Networks (original) (raw)

Abstract

We analyze a discrete-time quaternionic Hopfield neural network with continuous state variables updated asynchronously. The state of a neuron takes quaternionic value which is four-dimensional hypercomplex number. Two types of the activation function for updating neuron states are introduced and examined. The stable states of the networks are demonstrated through an example of small network.

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Authors and Affiliations

  1. Division of Computer Engineering, Graduate School of Engineering, University of Hyogo, Japan
    Teijiro Isokawa, Naotake Kamiura & Nobuyuki Matsui
  2. Graduate School of Applied Informatics, University of Hyogo, Japan
    Haruhiko Nishimura

Authors

  1. Teijiro Isokawa
  2. Haruhiko Nishimura
  3. Naotake Kamiura
  4. Nobuyuki Matsui

Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

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© 2007 Springer-Verlag Berlin Heidelberg

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Isokawa, T., Nishimura, H., Kamiura, N., Matsui, N. (2007). Dynamics of Discrete-Time Quaternionic Hopfield Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4\_86

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