Skip to main content
SpringerLink
Log in

A Type 2 Fuzzy Neural Network Ensemble to Estimate Time Increased Probability of Seismic Hazard in North Region of Baja California Peninsula

  • Chapter
  • First Online:
Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

  • 1371 Accesses

Abstract

A type-2 adaptive fuzzy neural network ensemble approach is presented here to achieve the prediction of seismic events of M0 magnitude in the north region of the Baja California Peninsula. Three algorithms are used with the ensemble: data analysis, M8 and CN. Seismic data coordinates are used in probabilistic fuzzy sets that are processed in the three fuzzy neural networks that integrate the ensemble to generate an output of a probabilistic set of predictions.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Resnom, G.: Sismicidad de la regiónnorte de Baja California, registradaporresnom en el periodoenero-diciembre de 2002 (CICESE). Unión Geofísica Mexicana, (2002)

    Google Scholar 

  2. Gutenberg, B., Richter, C. F.: Seismicity of the earth and associated phenomena, 2nd edn. Princeton University Press, Princeton (1954)

    Google Scholar 

  3. Yang, J., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng. 15(3), 613–628 (2003)

    Article  Google Scholar 

  4. Kanamori, H.: Earthquake prediction: an overview. Int. Handb. Earthquake Eng. Seismolog. 616, 1205–1216 (2003)

    Article  Google Scholar 

  5. Karnik, N. N., Mendel, J. M.: Introduction to type-2 fuzzy logic systems. In: Proceedings of the 1998 IEEE FUZZ Conference, Anchorage, pp. 915–920 (1998)

    Google Scholar 

  6. Keilis-Borok, V.I.: Intermediate-term earthquake prediction (premonitory seismicity patterns/dynamics of seismicity/chaotic systems/instability). In: Proceedings of the National Academy of Sciences USA, vol. 93, pp. 3748–3755. Colloquium paper (1996)

    Google Scholar 

  7. Keilis-Borok, V.I., Kossobokov, V.G.: Phys. Earth Planet. Inter. 61, 73–83 (1990)

    Article  Google Scholar 

  8. Keilis-Borok, V.I.: The algorithm M8. Rusian academic of sciences. http://www.mitp.ru/en/m8pred.html(2009)

  9. Main, I.: Earthquakes—long odds on prediction. Nature 385, 19–20 (1997)

    Article  Google Scholar 

  10. Monika, A. K.: Comparison of mamdani fuzzy model and neuro fuzzy model for load sensor. Int. J. Eng. Innovative. Technol. (IJEIT) 2(9), (2013)

    Google Scholar 

  11. Omori, F.: On the aftershocks of earthquakes. J Coll. Sci. 7, 111–200 (1894). Imperial University of Tokyo

    Google Scholar 

  12. Pulido, M., Melin, P., Castillo, O.: Optimization of type-2 fuzzy integration in ensemble neural networks for predicting the US Dolar/MX pesos time series. IEEE (978-1-4799-0348-1 2013) (2013)

    Google Scholar 

  13. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  14. Sepulveda, R., Castillo, O., Melin, P., Montiel, O.: An efficient computational method to implement type-2 fuzzy logic in control applications. Adv. Soft Comput. 41, 45–52 (2007)

    Article  Google Scholar 

  15. Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf. Sci. 177(10), 2023–2048 (2007)

    Article  Google Scholar 

  16. Sepulveda, R., Montiel, O., Lizarraga, G., Castillo, O.: Modeling and simulation of the defuzzification stage of a type-2 fuzzy controller using the xilinx system generator and simulink. In: Castillo, O., et al. (eds.) Studies in Computational Intelligence, vol. 257, pp. 309–325. Springer, Heidelberg (2009)

    Google Scholar 

  17. Sepulveda, R., Montiel, O., Castillo, O., Melin, P.: Optimizing the MFs in type-2 fuzzy logic controllers, using the human evolutionary model. Int. Rev. Autom. Control 3(1), 1–10 (2011)

    Google Scholar 

  18. Tsunekawa, H.: A fuzzy neural network prediction model of the principal motions of earthquakes based on preliminary tremors. IEEE (0-7803-4503-7) (1998)

    Google Scholar 

  19. Utsu, T.: A statistical study of the occurrence of aftershocks. Geophys. Mag. 30, 521–605 (1961)

    Google Scholar 

  20. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  21. Zamani, A., Sorbi, M.R., Safavi, A.A.: Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci. Inform. 6, 71–85 (2013). Springer, Heidelberg

    Article  Google Scholar 

  22. Zhou, Z., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, Mexico

    Victor M. Torres & Oscar Castillo

Authors
  1. Victor M. Torres
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Torres, V.M., Castillo, O. (2014). A Type 2 Fuzzy Neural Network Ensemble to Estimate Time Increased Probability of Seismic Hazard in North Region of Baja California Peninsula. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05170-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation