Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2008: Artificial Intelligence and Soft Computing – ICAISC 2008 pp 11–16Cite as

Parallel Realisation of the Recurrent RTRN Neural Network Learning

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

Abstract

In this paper we present a parallel realisation of Real-Time Recurrent Network (RTRN) learning algorithm. We introduce the cuboid architecture to parallelise computation of learning algorithms. Parallel neural network structures are explicitly presented and the performance discussion is included.

This is a preview of subscription content, 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   189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bilski, J., Litwiński, S., Smola̧g, J.: Parallel Realisation of QR Algorithm for Neural Networks Learning. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 158–165. Springer, Heidelberg (2004)

    Google Scholar 

  2. Kolen, J.F., Kremer, S.C.: A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, Los Alamitos (2001)

    Google Scholar 

  3. Korbicz, J., Patan, K., Obuchowicz, A.: Dynamic neural networks for process modelling in fault detection and isolation. Int. J. Appl. Math. Comput. Sci. 9(3), 519–546 (1999)

    MATH  Google Scholar 

  4. Smola̧g, J., Bilski, J.: A systolic array for fast learning of neural networks. In: Proc. of V Conf. Neural Networks and Soft Computing, Zakopane, pp. 754–758 (2000)

    Google Scholar 

  5. Smola̧g, J., Rutkowski, L., Bilski, J.: Systolic array for neural networks. In: Proc. of IV Conf. Neural Networks and Their Applications, Zakopane, pp. 487–497 (1999)

    Google Scholar 

  6. Williams, R., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 270–280 (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czȩstochowa University of Technology, Czȩstochowa, Poland

    Jarosław Bilski & Jacek Smola̧g

Authors
  1. Jarosław Bilski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek Smola̧g
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bilski, J., Smola̧g, J. (2008). Parallel Realisation of the Recurrent RTRN Neural Network Learning. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69731-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation