Design of Fuzzy Prediction System based on Dual Tuning using Enhanced Genetic Algorithms

강화된 유전알고리즘을 이용한 이중 동조 기반 퍼지 예측시스템 설계 및 응용

  • 방영근 (강원대 전기전자공하과) ;
  • 이철희 (강원대 전기전자공학부)
  • Published : 2010.01.01


Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.


  2. 김인택, 공창욱, "시계열 예측을 위한 퍼지 학습 알고리즘", 한국 퍼지 지능시스템 학회, vol.7, No.3, pp. 34-42, 1997
  3. 진강규, 유전알고리즘과 그 응용, 교우사, 2004
  4. K. Ozawa, T. Niimura, "Fuzzy Time-Series Model of Electric Power Consumption". IEEE Canadian Conference on Electrical and Computer Engineering, vol. 2, pp.1195-1198, 1999
  5. S. S. Cheng, Y. H. Chao, H. M. Wang, H. C. Fu, "A Prototype-Embedded Genetic K-means Algorithm", ICPR. 2006. 18th International Conference on Pattern Recognition, vol. 2, pp.724-727, 2006
  6. K. Venkatalakshmi, P. A. Praisy, R. Maragathavalli, S. M. Shalinie, "Multispectral Image Clustering Using Enhanced Genetic k-Means Algorithm", Information Technology Journal, vol.6, pp.557-560, 2007
  7. L. X. Wang, J. M. Mendel, "Generating fuzzy rules from numerical data, with applications", IEEE Trans. on Systems, Man, and Cybern, 22 No.6, pp1414-1427, 1992
  8. 주용석, 유전알고리즘과 러프집합을 이용한 퍼지 시스템 모델링, 강원대학교 석사학위논문, 2003
  9. 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
  10. D. J. Kim, C. H. Kim, "Forecasting Time Series with Genetic Fuzzy Predictor Ensemble". IEEE Trans. on Fuzzy Systems, vol. 5, pp.523-535, 1997
  11. K. J. Kim, C. H. Kim, "Using a Clustering Genetic Algorithm to Support Customer Segmentation for Persoonalized Recommender Systems", LNCS, vol. 3397, pp. 409-415, 2005
  12. I. T. Kim, S. R. Lee "A Fuzzy Time Series Prediction Method Based on Consecutive Values", IEEE International Conference on Fuzzy Systems, vol.2 , pp. 703-707, 1999
  13. Y. K. Bang, C. H. Lee "Fuzzy Time Series prediction with Data Preprocessing and Error Compensation Based on Correlation Analysis", International Conference on Convergence and Hybrid Information Technology, vol.2, pp.714-721, 2008