Building a fuzzy model with transparent membership functions through constrained evolutionary optimization

International Journal of Control, Automation and Systems

Volumn 2, Issue 3, 2004, Pages 298-309

  Kim, Min Soeng ^a   Kim, Chang Hyun ^a   Lee, Ju Jang ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Evolutionary algorithm; Model interpretability; Takagi Sugeno Kang fuzzy model; Time series prediction

Indexed keywords

CONSTRAINT THEORY; EVOLUTIONARY ALGORITHMS; FUNCTIONS; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING;

MODEL INTERPRETABILITY; TAKAGI-SUGENO-KANG FUZZY MODEL; TIME SERIES PREDICTION; TRANSPARENT MEMBERSHIP FUNCTIONS;

FUZZY CONTROL;

EID: 4544306325     PISSN: 15986446     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Y. Shi, R. Eberhart, and Y. Chen, "Implementation of evolutionary fuzzy systems," IEEE Trans. on Fuzzy Syst., vol. 7, no. 2, pp. 109-119, 1999.
2. M. Russo, "Genetic fuzzy learning," IEEE Trans. on Evolutionary Computation., vol. 4, no. 3, pp. 259-273, 2000.
3. O. Cordó n and F. Herrera, "A two-stage evolutionary process for designing TSK fuzzy rule-based systems," IEEE Trans. on Syst., Man, Cybern., pt, B, vol. 29, pp. 703-715, December 1999.
4. S. J. Kang, H. S. Hwang, and K. N. Woo, "Evolutionary design of fuzzy rule base for nonlinear system modeling and control," IEEE Trans. on Fuzzy Syst., vol. 8, pp. 37-45, February 2000.
5. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. on Syst., Man, Cybern., vol. 15, pp. 116-132, 1985.
6. M. Setnes and H. Roubos, "GA-fuzzy modeling and classification: Complexity and performance," IEEE Trans. on Fuzzy Syst., vol. 8, pp. 509-522, October 2000.
7. J. Yen, L. Wang, and C. W. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning," IEEE Trans. on Fuzzy. Syst., vol. 6, pp. 530-537, November 1998.
8. M.-S. Kim and J.-J. Lee, "Evolutionary design of takagi-sugeno type fuzzy model for nonlinear system identification and time series prediction," Proc. on of the International Conference on Control, Automation and Systems, Cheju Univ. Korea, pp. 667-670, October 2001.
9. Z. Michalewicz, Genetic algorithms + Data Stuructures = Evolution Programs, Springer, New York, 1999.
10. J. Yen and L. Wang, "Application of statistical information criteria for optimal fuzzy construction," IEEE Trans. on Fuzzy Syst., vol. 6, pp. 362-371, August 1998.
11. J. Yen and L. Wang, "Simplifying fuzzy rule based models using orthogonal transformation methods," IEEE Trans. on Syst., Man, Cybern., pt, B, vol. 29, pp. 13-24, February 1999.
12. M. C. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol. 197, pp. 287-289, 1977.
13. D. Kim and C. Kim, "Forecasting time series with genetic fuzzy predictor ensemble," IEEE Trans. on Fuzzy Syst., vol. 5, pp. 523-535, November 1997.
14. M. R. Delgado, F. V. Zuben, and F. Gomide, "Multi-objective decision making towards improvement of accuracy interpretability and design autonomy in hierarchical genetic fuzzy systems," FUZZ-IEEE"02. Proc. of the 2002 IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1222-1227, 2002.
15. Y. Jin, "Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement," IEEE Trans. on Fuzzy Syst., vol. 8, no. 2, pp. 212-221, 2000
16. J.-S.R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. on Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, 1993.
17. Simon S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.