Skip to main content
View expanded cover

International Conference on Multimedia and Network Information System

MISSI 2018: Multimedia and Network Information Systems pp 503–511Cite as

Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 833)

Abstract

This paper is focused on weight adaptation stability analysis of static and dynamic neural units for prediction applications. The aim of this paper is to provide verifiable conditions in which the weight system is stable during sample-by-sample adaptation. The paper presents a novel approach toward stability of linear and higher-order neural units. A study of utilization of linear and higher-order neural units with the foundations on stability of the gradient descent algorithm for static and dynamic models is addressed.

Keywords

  • Linear neural units
  • Higher-order neural units
  • Stability analysis

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-98678-4_50
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-98678-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

References

  1. Rodríguez Jorge, R.: Lung tumor motion prediction by neural networks. Ph.D. thesis. Czech Technical University in Prague, Czech Republic (2012)

    Google Scholar 

  2. Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks. Wiley, U.S.A (2003)

    CrossRef  Google Scholar 

  3. Bach, F.: Breaking the curse of dimensionality with convex neural networks. J. Mach. Learn. Res. 18, 1–53 (2017)

    MATH  Google Scholar 

  4. Rodriguez, R., Vergara Villegas, O.O., Cruz Sanchez, V.G., Bila, J., Mexicano, A.: Arrhythmia disease classification using a higher-order neural unit. In: 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), pp. 1–6. IEEE, July 2015

    Google Scholar 

  5. Rodríguez, R., Mexicano, A., Bila, J., Ponce, R., Cervantes, S., Martinez, A.: Hilbert transform and neural networks for identification and modeling of ECG complex. In: 2013 Third International Conference on Innovative Computing Technology (INTECH), pp. 327–332. IEEE, August 2013

    Google Scholar 

  6. Cancino, E., Rodriguez Jorge, R., Vergara Villegas, O.O., Cruz Sánchez, V.G., Bila, J., Nandayapa, M., Israel, P., Soto, A., Abad, A.: Monitoring of cardiac arrhythmia patterns by adaptive analysis. In: The 11th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2016) (2016)

    Google Scholar 

  7. Mironovova, M., Bíla, J.: Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals. In: 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), pp. 1–6. IEEE, July 2015

    Google Scholar 

  8. Rodriguez, R., Bukovsky, I., Bila, J.: Weight Adaptation Stability of Static Quadratic Neural Unit. Ústav Přístrojové a řídicí techniky, pp. 83–88. Czech Technical University in Prague (2011)

    Google Scholar 

  9. Rodríguez Jorge, R., Martínez García, E., Mizera-Pietraszko, J., Bila, J., Torres Córdoba, R.: Prediction of highly non-stationary time series using higher-order neural units. In: Xhafa, F., Caballé, S., Barolli, L. (eds.) Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol. 13. Springer, Cham (2017)

    Google Scholar 

Download references

Acknowledgements

The project is supported by a research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

Author information

Authors and Affiliations

  1. Institute of Engineering and Technology, Universidad Autónoma de Ciudad Juárez, Partido Romero, 32310, Ciudad Juárez, Mexico

    Ricardo Rodriguez-Jorge & Edgar A. Martínez-Garcia

  2. Czech Technical University in Prague, Technická 4, 166 07 Praha 6, Prague, Czech Republic

    Jiri Bila

  3. Institute of Mathematics and Computer Science, Opole University, Opole, Poland

    Jolanta Mizera-Pietraszko

Authors
  1. Ricardo Rodriguez-Jorge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiri Bila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jolanta Mizera-Pietraszko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Edgar A. Martínez-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Rodriguez-Jorge .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Kazimierz Choroś

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Marek Kopel

  3. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Elżbieta Kukla

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Siemiński

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Rodriguez-Jorge, R., Bila, J., Mizera-Pietraszko, J., Martínez-Garcia, E.A. (2019). Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_50

Download citation