DOI QR코드

DOI QR Code

Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase

  • Wang, Bo-Hyeun (Department of Electrical Engineering, Kangnung National University) ;
  • Cho, Hyun-Joon (Department of Electrical and Computer Engineering, Purdue University)
  • 발행 : 2007.04.25

초록

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

키워드

참고문헌

  1. M. Sugeno and T. Yasukawa, 'A fuzzy-logic-based approach to qualitative modeling,' IEEE Trans. Fuzzy Systems, vol. 1, no. 1, pp. 7-31, Feb. 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  2. M. Kubat, 'Decision trees can initialize radial basis function networks,' IEEE Trans. Neural Networks, Vol. 9, No.5, pp. 813-821, Sept. 1998 https://doi.org/10.1109/72.712154
  3. Y. J. Park, H. J. Shim, and B. H. Wang, 'Short-term electrical load forecastir.g using neuro-fuzzy models,' The Transaction of KIEE, Vol. 49A, No.3, pp. 107-117,2000
  4. B. H. Wang and H. J. Cho, 'Structure identification of neuro-fuzzy models using genetic algorithms,' in Proc. 8th Int. Symp. Artificial Life and Robotics, (Oita, Japan) pp. 573-576, Jan. 2003
  5. A. Lotti and A. C. Tsoi, 'Learning fuzzy inference systems using an adaptive membership function scheme,' IEEE Trans. Syst., Man, and Cybern., vol. SMC-26, pp. 326-331, April 1996
  6. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum Press, 1981
  7. W. Pedrycz, Fuzzy Control and Fuzzy Systems. New York: Wiley, 1989
  8. X. Wang, B. D. Baets, and E. Kerre, 'A comparative study of similarity measures,' Fuzzy Sets and Syst., vol. 73, pp. 259-268, 1995 https://doi.org/10.1016/0165-0114(94)00308-T
  9. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, San Francisco: Holden Day, 1976
  10. S. Lee and R. M. Kil, 'A Gaussian potential function network with hierarchically self-organizing learning,' Neural Networks, vol. 4, pp. 207-224, 1991 https://doi.org/10.1016/0893-6080(91)90005-P
  11. P. D. Wasserman, Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, 1993
  12. Bien and Yu, 'Extracting core information from inconsistent fuzzy control rules,' Fuzzy Sets and Syst., vol. 71, no. I, pp. 95-111, 1995 https://doi.org/10.1016/0165-0114(94)00191-9
  13. L. X. Wang and J. M. Mendel, 'Generating fuzzy rules by learning from example,' IEEE Trans. Syst., Man, Cybern., vol. SMC-22, no. 6, pp. 1414-1427, 1992
  14. J. S. R. lang, 'ANFIS: Adaptive-network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., vol. SMC-23, no. 3, pp. 665-684, May/June 1993