전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제60권7호
  • /
  • Pages.1396-1403
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  • 2011
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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HCBKA 기반 IT2TSK 퍼지 예측시스템 설계

Design of HCBKA-Based IT2TSK Fuzzy Prediction System

  • 방영근 (강원대학교 삼척캠퍼스 전기공학과) ;
  • 이철희 (강원대학교 전기전자공학과)
  • 투고 : 2011.03.08
  • 심사 : 2011.05.14
  • 발행 : 2011.07.01
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초록

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

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References

  1. G. Box, G. M. Jenkins, G. C. Reinsel, Time Series Analysis : Forecasting and Control, third edition, Prentice-Hall, 1994. Design Automation Conf., pp. 253-259, 1992.
  2. 김해경, 김태수, 시계열 분석과 예측 이론, 교우사, 2003. Systems, pp. 446-455, Dec. 1994.
  3. D. M. Kim, I. S. Bae, J. M. Cho, J. O. Kim, K. C. Chang, "Prediction of Dynamic Line Rating Based on Thermal Risk Probability by Time Series Weather Models", Trans. KIEE, vol. 55A, no. 7, pp. 273-280, 2006.
  4. G, Janazcek, L. Swift, Time Series Forecasting, Simulation, Application, Ellis Horwood, 1993.
  5. T. Senjyu, H. Takara, K. Uezato, T. Funabashi, "One-Hour-Ahead Load Forecasting Using Neural Network", IEEE Trans Power Syst, vol. 17, pp. 113-118, 2002. https://doi.org/10.1109/59.982201
  6. O. Valenzuela, I. Rojas, F. Rojas, H. Pomares, L. J. Herrera, A. Guillen, L, Marquez, M. Pasadas, "Hybridization of intelligents and ARIMA models for time series prediction", Fuzzy Sets and Systems, vol. 159, pp. 821-845, 2008. https://doi.org/10.1016/j.fss.2007.11.003
  7. M. EI-Koujok, R. Gouriveau, N. Zerhouni, "Towards a Neuro-Fuzzy System for Time Series Forecasting in Maintenance Applications", 17th Triennal Word Congress of the International Federation of Automatic Control, hal-00298361, version 1, 2008.
  8. Y. K. Bang, C. H. Lee, "Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application", Trans. KIEE, vol. 58, pp. 173-180, 2009.
  9. Y. K. Bang, C. H. Lee, "Design of Fuzzy System with Hierarchical Classifying Structure and its Application to Time Series Prediction", Journal of Korean Institute of Intelligent Systems, vol. 19, pp. 595-602, 2009. https://doi.org/10.5391/JKIIS.2009.19.5.595
  10. L. A. Zadeh, "A computational approach to fuzzy quantifiers in natural languages", Comput. Math, vol. 9, pp. 149-184, 1983.
  11. J. M. Mendel, R. I. John, "Type-2 Fuzzy Sets Made Simple", IEEE Trans. on Fuzzy Systems, vol. 10, pp. 117-127, 2002. https://doi.org/10.1109/91.995115
  12. J. M. Mendel, R. I. John, F. Lui, "Interval Type-2 Fuzzy Logic Systems Made Simple", IEEE Trans. on Fuzzy Systems, vol. 14, pp. 808-821, 2006. https://doi.org/10.1109/TFUZZ.2006.879986
  13. N. N. Karnik, J. M. Mendel, "Centroid of a Type-2 Fuzzy Set", Information Sciences, vol. 132, pp. 195-200, 2001. https://doi.org/10.1016/S0020-0255(01)00069-X
  14. http://www.personal.buseco.monash.edu.au/hyndman
  15. Y. S. Joo, Fuzzy System Modeling Using Genetic Algorithm and Rough Set Theory, M. S. Thesis, Dept. of Electrical and Electronic Eng, Kangwon Univ, Korea, 2003. 저