Skip to main content
SpringerLink
Log in

One-to-Many Association Ability of Chaotic Quaternionic Multidirectional Associative Memory

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

Included in the following conference series:

Abstract

In this paper, we investigate one-to-many association ability of multi-valued patterns in the Chaotic Quaternionic Multidirectional Associative Memory (CQMAM). The CQMAM is based on the Multidirectional Associative Memory and composed of quaternionic neurons and chaotic quaternionic neurons, it can realize one-to-many associations of M-tuple multi-valued patterns. Although the conventional Chaotic Complex-valued Multidirectional Associative Memory with variable scaling factor (CCMAM) can realize one-to-many associations of M-tuple multi-valued patterns, the one-to-many association ability of the CQMAM is better than that of the conventional CCMAM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yano, Y., Osana, Y.: Chaotic complex-valued bidirectional associative memory. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Atlanta (2009)

    Google Scholar 

  2. Yano, Y., Osana, Y.: Chaotic complex-valued bidirectional associative memory – one-to-many association ability. In: Proceedings of International Symposium on Nonlinear Theory and its Applications, Sapporo (2009)

    Google Scholar 

  3. Shimizu, Y., Osana, Y.: Chaotic complex-valued multidirectional associative memory. In: Proceedings of IASTED Artificial Intelligence and Applications, Innsbruck (2010)

    Google Scholar 

  4. Yoshida, A., Osana, Y.: Chaotic complex-valued multidirectional associative memory with variable scaling factor. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 266–274. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Jankowski, S., Lozowski, A., Zurada, J.M.: Complex-valued multistate neural associative memory. IEEE Transactions on Neural Networks 7(6), 1491–1496 (1996)

    Article  Google Scholar 

  6. Nakada, M., Osana, Y.: Chaotic complex-valued associative memory. In: Proceedings of International Symposium on Nonlinear Theory and its Applications, Vancouver (2007)

    Google Scholar 

  7. Okutsu, T., Osana, Y.: Chaotic quaternionic multidirectional associative memory. In: Proceedings of International Symposium on Nonlinear Theory and its Applications, Luzern (2014)

    Google Scholar 

  8. Isokawa, T., Nishimura, H., Kamiura, N., Matsui, N.: Fundamental properties of quaternionic Hopfield neural network. International Journal of Neural Systems 18(2), 135–145 (2008)

    Article  Google Scholar 

  9. Osana, Y.: Chaotic quaternionic associative memory. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Brisbane (2012)

    Google Scholar 

  10. Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Physics Letter A 144(6 & 7), 333–340 (1990)

    Article  MathSciNet  Google Scholar 

  11. Osana, Y.: Recall and separation ability of chaotic associative memory with variable scaling factor. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Hawaii (2002)

    Google Scholar 

  12. Hagiwara, M.: Multidirectional associative memory. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Washington, D.C., vol. 1, pp. 3–6 (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tokyo University of Technology, 1401-1 Katakura, Hachioji, Tokyo, Japan

    Takumi Okutsu & Yuko Osana

Authors
  1. Takumi Okutsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuko Osana
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Okutsu, T., Osana, Y. (2014). One-to-Many Association Ability of Chaotic Quaternionic Multidirectional Associative Memory. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12637-1_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation