Pattern Retrieval by Quaternionic Associative Memory with Dual Connections

  • Toshifumi MinemotoEmail author
  • Teijiro Isokawa
  • Masaki Kobayashi
  • Haruhiko Nishimura
  • Nobuyuki Matsui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


An associative memory based on Hopfield-type neural network, called Quaternionic Hopfield Associative Memory with Dual Connection (QHAMDC), is presented and analyzed in this paper. The state of a neuron, input, output, and connection weights are encoded by quaternion, a class of hypercomplex number systems with non-commutativity for its multiplications. In QHAMDC, calculation for an internal state of a neuron is conducted by two types of multiplications for neuron’s output and connection weight. This makes robustness of the proposed associative memory for retrieval of patterns. The experimental results show that the performances of retrieving patterns by QHAMDC are superior to those by the previous QHAM.



This study was financially supported by Japan Society for the Promotion of Science (Grant-in-Aids for Scientific Research (C) 16K00337).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Toshifumi Minemoto
    • 1
    Email author
  • Teijiro Isokawa
    • 1
  • Masaki Kobayashi
    • 2
  • Haruhiko Nishimura
    • 3
  • Nobuyuki Matsui
    • 1
  1. 1.Graduate School of EngineeringUniversity of HyogoHimejiJapan
  2. 2.Yamanashi UniversityKofuJapan
  3. 3.Graduate School of Applied InformaticsUniversity of HyogoKobeJapan

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