Parallel Realisation of the Recurrent Multi Layer Perceptron Learning | SpringerLink
Skip to main content
Springer Nature Link
Log in

Parallel Realisation of the Recurrent Multi Layer Perceptron Learning

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

Included in the following conference series:

Abstract

This paper presents the parallel architecture of the Recurrent Multi Layer Perceptron learning algorithm. The proposed solution is based on the high parallel three dimensional structure to speed up learning performance. Detailed parallel neural network structures are explicitly shown.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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.

Similar content being viewed by others

References

  1. Bilski, J., Litwiński, S., Smoląg, 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)

    Chapter  Google Scholar 

  2. Bilski, J., Smoląg, J.: Parallel Realisation of the Recurrent RTRN Neural Network Learning. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 11–16. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Bilski, J., Smoląg, J.: Parallel Realisation of the Recurrent Elman Neural Network Learning. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 19–25. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

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

    Google Scholar 

  5. 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 

  6. Smoląg, 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 

  7. Smoląg, 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 

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

    Google Scholar 

  9. Korytkowski, M., Scherer, R., Rutkowski, L.: On Combining Backpropagation with Boosting. In: International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, pp. 1274–1277 (2006)

    Google Scholar 

  10. Korytkowski, M., Rutkowski, L., Scherer, R.: From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Nowicki, R.: Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 510–517. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Nowicki, R.: Rough Sets in the Neuro-Fuzzy Architectures Based on Non-monotonic Fuzzy Implications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 518–525. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Rutkowski, L., Cpałka, K.: A general approach to neuro - fuzzy systems. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, December 2-5, vol. 3, pp. 1428–1431 (2001)

    Google Scholar 

  14. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control and Cybernetics 31(2), 297–308 (2002)

    MATH  Google Scholar 

  15. Scherer, R.: Boosting Ensemble of Relational Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Scherer, R.: Neuro-fuzzy Systems with Relation Matrix. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 210–215. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  17. Starczewski, J., Rutkowski, L.: Neuro-Fuzzy Systems of Type 2. In: Proc. 1st Int’l Conf. on Fuzzy Systems and Knowledge Discovery, Singapore, vol. 2, pp. 458–462 (2002)

    Google Scholar 

  18. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  19. Starczewski, J.T., Rutkowski, L.: Connectionist Structures of Type 2 Fuzzy Inference Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Jarosław Bilski & Jacek Smoląg

Authors
  1. Jarosław Bilski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek Smoląg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bilski, J., Smoląg, J. (2012). Parallel Realisation of the Recurrent Multi Layer Perceptron Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29347-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation