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A Multidirectional Associative Memory Based on Self-organizing Incremental Neural Network

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Neural Information Processing. Models and Applications (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6444))

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Abstract

A multidirectional associative memory (AM) is proposed. It is constructed with three layer networks: an input layer, a memory layer, and an associate layer. The proposed method is able to realize many-to-many associations with no predefined conditions, and the association can be incrementally added to the network without destruction of old associations. Experiments show that the proposed AM works well for real tasks.

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Author information

Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, China

    Hui Yu & Furao Shen

  2. Jiangyin Information Technology Research Institute, Nanjing University, China

    Hui Yu & Furao Shen

  3. Imaging Science and Engineering Lab., Tokyo Institute of Technology, Japan

    Osamu Hasegawa

Authors
  1. Hui Yu
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  2. Furao Shen
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  3. Osamu Hasegawa
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Yu, H., Shen, F., Hasegawa, O. (2010). A Multidirectional Associative Memory Based on Self-organizing Incremental Neural Network. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_42

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  • DOI: https://doi.org/10.1007/978-3-642-17534-3_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17533-6

  • Online ISBN: 978-3-642-17534-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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