Abstract
This paper presents a type-2 adaptive fuzzy neural network ensemble to predict chaotic time series in combination with the well known M8 algorithm. The chaotic time series is depicted by the register of seismic events and their seismic coordinates in a catalog. ANFIS model are used as components of the Ensemble to train and evaluate seven chaotic time series that are used by the M8 algorithm to make a prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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, Berlin
Bolt, B.A.: Earthquakes and Geological Discovery. Scientific American Library, New York (1993)
Soto, J., Melin, P., Castillo, O.: Time series prediction using ensembles of neuro-fuzzy models with interval type-2 and type-1 fuzzy integrators. In: Conference: IJCNN 2013, Dallas Texas (2013)
Tsunekawa, H.: A Fuzzy Neural Network Prediction Model of the Principal Motions of Earthquakes Based on Preliminary Tremors. IEEE, New Jersey (1998). ISBN: 0-7803-4503-7
Utsu, T.: A statistical study of the occurrence of aftershocks. Geophys. Mag. 30, 521–605 (1961)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Karnik, N.N., Mendel, J.M.: Introduction to type-2 fuzzy logic systems. In: Proceedings in 1998 IEEE FUZZY Conference, pp. 915–920. Anchorage, May 1998
Monika, A.K.: Comparison of mamdani fuzzy model and neuro fuzzy model for load sensor. Int. J. Eng. Innovative Technol. (IJEIT) 2(9) (2013)
Chen D.W., Zhang, J.-P.: Time series prediction based on ensemble ANFIS. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18–21 August 2005
Zhou, Z., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)
Dietterich, T.G.: Machine learning research: four current directions. Artif. Intell. 18(4), 97–136 (1998)
Yang, J., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng. 15(3) (2003)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Keilis-Borok, V.I., Kossobokov, V.G.: Premonitory activation of seismic flow: algorithm M8. Phys. Earth Planet. Int. 61, 73–83 (1990)
Keilis-Borok, V.I., Knopoff, L., Rotvain, I.M.: Nature 283, 259–263 (1980)
Gutenberg, B., Richter, C.F.: Seismicity of the Earth and Associated Phenomena, 2nd ed. Princeton University Press, Princeton (1954)
Omori, F.: On the aftershocks of earthquakes. J. Coll. Sci. Imperial Univ. Tokyo 7, 111–200 (1894)
Keilis-Borok, V.I.: The Algorithm M8. Russian Academic of Sciences. http://www.mitp.ru/en/m8pred.html (2009)
Thomas, A.M.: Economic impacts of earthquake prediction. In: Proceedings of the Seminar on Earthquake Prediction Case Histories, pp. 179–185. UNDPRO, Geneva, 12–15 October 1982 (1983)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Torres, V.M., Castillo, O. (2015). A Type-2 Fuzzy Neural Network Ensemble to Predict Chaotic Time Series. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-17747-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17746-5
Online ISBN: 978-3-319-17747-2
eBook Packages: EngineeringEngineering (R0)