Parallel-Structure Fuzzy System for Sunspot Cycle Prediction in the Railway Systems

  • Min-Soo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


This paper presents a parallel-structure fuzzy system (PSFS) for prediction of sunspot cycle in the railway communication and power systems based on smoothed sunspot number time series. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.


Fuzzy System Fuzzy Rule Cluster Center Sunspot Number Time Series Prediction 
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  1. 1.
    Weigend, A.S., Gershenfeld, N.A. (eds.): Time Series Prediction: Forecasting the Future and Understanding the Past, pp. 175–193. Addison-Wesley, Reading (1994)Google Scholar
  2. 2.
    Kim, M.S., Lee, H.S., You, C.H., Chung, C.S.: Chaotic Time Series Prediction using PSFS2. In: 41st Annual Conference on SICE (August 2002)Google Scholar
  3. 3.
    Thompson, R.J.: A Technique for Predicting the Amplitude of the Solar Cycle. Solar Physics 148 (1993)Google Scholar
  4. 4.
    Hathaway, D.H., Wilson, R.M., Reichmann, E.J.: A Synthesis of Solar Cycle Prediction Techniques. Journal Geophys. Res. 104(A10) (1999)Google Scholar
  5. 5.
    Li, K.J., Yun, H.S., Liang, H.F., Gu, X.M.: Solar activity in Extended Cycles. Journal Geophys. Res. 107(A7) (2002)Google Scholar
  6. 6.
    Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier Science, Amsterdam (1985)Google Scholar
  7. 7.
    Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. Int. J. of Man Machine Studies 7(1), 1–13 (1975)MATHCrossRefGoogle Scholar
  8. 8.
    Casdagal, M.: Nonlinear Prediction of Chaotic Time Series. Physica D, 335–356 (1989)Google Scholar
  9. 9.
    Lowe, D., Webb, A.R.: Time Series Prediction by Adaptive Networks: A Dynamical Systems Perspective. In: Vemuri, V.R., Rogers, R.D. (eds.) Artificial Neural Networks, Forecasting Time Series, pp. 12–19. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  10. 10.
    Broomhead, D.S., Lowe, D.: Multi-variable Functional Interpolation and Adaptive Networks. Complex Systems, 262–303 (1988)Google Scholar
  11. 11.
    Kim, M.-S., Kong, S.-G.: Time Series Prediction using the Parallel-Structure Fuzzy System. In: 1999 IEEE Int. Fuzzy Systems Conference Proceedings, August 1999, vol. 2, pp. 934–938 (1999)Google Scholar
  12. 12.
    Kim, M.S., Kong, S.G.: Parallel Structure Fuzzy Systems for Time Series Prediction. Int. Journal of Fuzzy Systems 3(1) (March 2001)Google Scholar
  13. 13.
    Jang, J.-S.R., Sun, C.-T.: Neuro-Fuzzy Modeling and Control. Proceedings of the IEEE (March 1995)Google Scholar
  14. 14.
    Chiu, S.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems 2(3) (September 1994)Google Scholar
  15. 15.
    Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control, pp. 246–264. John Wiley & Sons, Chichester (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min-Soo Kim
    • 1
  1. 1.Korea Railroad Research InstituteMaglev Train System Research TeamUiwang-City, Kyonggi-DoKorea

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