Skip to main content

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 13)

Abstract

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-69835-9_74
  • 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   299.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-69835-9
  • 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   379.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Gupta, M.M., Liang, J., Homma, N.: Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory. Wiley, New Jersey (2003)

    CrossRef  Google Scholar 

  2. Rodriguez, R., Bukovsky, I., Homma, N.: Potentials of quadratic neural unit for applications. In: Advances in Abstract Intelligence and Soft Computing, p. 343 (2012)

    Google Scholar 

  3. Rodríguez, R., Bila, J., Mexicano, A., Cervantes, S., Ponce, R., Nghien, N.B.: Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction. In: 2014 10th International Conference on Natural Computation (ICNC), pp. 561–567. IEEE (2014)

    Google Scholar 

  4. Rodriguez, 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 (2013)

    Google Scholar 

  5. Herrera, J.E.C., Rodriguez Jorge, R., Vergara Villegas, O.O., Cruz Sánchez, V.G., Bila, J., de Jesús Nandayapa Alfaro, M., Ponce, I.U., Soto Marrufo, A.I.: Monitoring of cardiac arrhythmia patterns by adaptive analysis. In: Xhafa, F., Barolli, L., Amato, F. (eds.) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol. 1, pp. 885–894. Springer, Cham (2016)

    CrossRef  Google Scholar 

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

    Google Scholar 

  7. Martinez Molina, A., Rodriguez Jorge, R., Villa-Angulo, R., Bila, J., Mizera-Pietraszko, J., Torres Arguelles, S.: Review on higher-order neural units to monitor cardiac arrhythmia patterns. In: Advances in Digital Technologies: Proceedings of the 8th International Conference on Applications of Digital Information and Web Technologies 2017, vol. 295, pp. 219–231. IOS Press (2017)

    Google Scholar 

  8. Rodriguez Jorge, R., Bila, J., Mizera-Pietraszko, J., Loya Orduño, R.E., Martinez Garcia, E., Torres Córdoba, R.: Adaptive methodology for designing a predictive model of cardiac arrhythmia symptoms based on cubic neural unit. In: Advances in Digital Technologies: Proceedings of the 8th International Conference on Applications of Digital Information and Web Technologies 2017, vol. 295, pp. 232–239. IOS Press (2017)

    Google Scholar 

  9. Rodriguez Jorge, R.: Artificial neural networks: challenges in science and engineering applications. In: Advances in Digital Technologies: Proceedings of the 8th International Conference on Applications of Digital Information and Web Technologies 2017, vol. 295, pp. 25–35. IOS Press (2017)

    Google Scholar 

  10. Gupta, M.M.: Correlative type higher-order neural units with applications. In: Proceedings of the IEEE International Conference on Automation and Logistics, Qingdao, China (2008)

    Google Scholar 

  11. Gupta, M.M.: Development of higher order neural units for control and pattern recognition. In: 2005 Annual Meeting of the North American Fuzzy Information Processing Society, Ann Arbor, Michigan (2005)

    Google Scholar 

  12. Hou, Z.-G., Song, K.-Y., Gupta, M.M., Tan, M.: Neural units with higher-order synaptic operations for robotic image processing applications. Soft. Comput. 11(3), 221–228 (2007)

    CrossRef  Google Scholar 

  13. Morgado, D.F., Antunes, A., Vieira, J., Mota, A.: Implementing the Levenberg-Marquardt algorithm on-line: a sliding window approach with early stopping. In: 2nd IFAC Workshop on Advanced Fuzzy/Neural Control (2004)

    Google Scholar 

  14. Rodriguez Jorge, R.: Lung tumor motion prediction by neural networks. Ph.D. thesis, Czech Technical University in Prague, Faculty of Mechanical Engineering, Department of Instrumentation and Control Engineering, November 2012

    Google Scholar 

  15. Morgado, D.F., Antunes, A., Vieira, J., Mota, A.: On-line training of neural networks: a sliding window approach for the Levenberg-Marquardt algorithm. In: International Work-Conference on the Interplay between Natural and Artificial Computation. LNCS, Las Palmas, Spain, vol. 3562 (2005)

    Google Scholar 

  16. Suratgar, A.A., Tovakoli, M.B., Hoseinabadi, A.: Modified Levenberg-Marquardt method for neural networks training. World Academy of Science, Engineering and Technology 6, 46–48 (2005)

    Google Scholar 

Download references

Acknowledgements

This project is supported by Research Grant No. DSA/103.5/16/10473 awarded by PRODEP and the Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ricardo Rodríguez Jorge .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Rodríguez Jorge, R., Martínez García, E., Mizera-Pietraszko, J., Bila, J., Torres Córdoba, R. (2018). 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. https://doi.org/10.1007/978-3-319-69835-9_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69835-9_74

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69834-2

  • Online ISBN: 978-3-319-69835-9

  • eBook Packages: EngineeringEngineering (R0)