Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation

Applied Mathematical Modelling

Volumn 33, Issue 6, 2009, Pages 2791-2807

  Choi, Jeoung Nae ^a   Oh, Sung Kwun ^b   Pedrycz, Witold ^c,d  

^a Daelim University   (South Korea)

^b Suwon University   (South Korea)

^c UNIVERSITY OF ALBERTA   (Canada)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

C Means; Fuzzy relation model; Genetic algorithms; Hierarchical fair competition (HFC); Information granulation; Multi population genetic optimization

Indexed keywords

COMPETITION; CURVE FITTING; ELECTRIC NETWORK PARAMETERS; FUZZY INFERENCE; GENETIC ALGORITHMS; GRANULATION; HIERARCHICAL SYSTEMS; HYBRID SYSTEMS; INFERENCE ENGINES; MEMBERSHIP FUNCTIONS; PARALLEL ALGORITHMS; PARAMETER ESTIMATION; POPULATION STATISTICS; STANDARDIZATION; STRUCTURAL OPTIMIZATION;

C-MEANS; FUZZY RELATION MODEL; HIERARCHICAL FAIR COMPETITION (HFC); INFORMATION GRANULATION; MULTI-POPULATION GENETIC OPTIMIZATION;

MATHEMATICAL MODELS;

EID: 60649117130     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2008.08.022     Document Type: Article

References (38)

1. Pedrycz W. An identification algorithm in fuzzy relational system. Fuzzy Sets Syst. 13 (1984) 153-167
2. Czogola E., and Pedrycz W. On identification in fuzzy systems and its applications in control problems. Fuzzy Sets Syst. 6 (1981) 73-83
3. Tong R.M. The evaluation of fuzzy models derived from experimental data. Fuzzy Sets Syst. 13 (1980) 1-12
4. Xu C.W. Fuzzy system identification. IEE Proc. 126 4 (1989) 146-150
5. Xu C.W., and Zailu Y. Fuzzy model identification self-learning for dynamic system. IEEE Trans. Syst., Man, Cybernet. 17 4 (1987) 683-689
6. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybernet. 15 1 (1985) 116-132
7. M. Sugeno, T. Yasukawa, Linguistic modeling based on numerical data, in: IFSA'91 Brussels, Computer, Management and System Science, 1991, pp. 264-267.
8. Ismail M.A. Soft clustering algorithm and validity of solutions. In: Gupta M.M. (Ed). Fuzzy Computing Theory, Hardware and Applications (1988), North Holland 445-471
9. Oh S.K., and Pedrycz W. Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems. Fuzzy Sets Syst. 115 2 (2000) 205-230
10. Chung W.Y., and Kim E.T. A new two-phase approach to fuzzy modeling for nonlinear function approximation. IEICE Trans. Info. Syst. E89-D 9 (2006) 2473-2483
11. Liu F., Lu P., and Pei R. A new fuzzy modeling and identification based on fast-cluster and genetic algorithm. Intell. Contr. Automat. 1 (2004) 290-293
12. Andreas B. Identifying fuzzy models utilizing genetic programming. Fuzzy Sets Syst. 113 (2000) 333-350
13. Manderick B., and Spiessens P. Fine-grained parallel genetic algorithms. In: Schaffer J.D. (Ed). Proceeings of the Third International Conference on Genetic Algorithms (1989), Morgan Kaufmann, San Mateo, CA 428-433
14. S.-C. Lin, E. Goodman, W. Punch, Coarse-grain parallel genetic algorithms: categorization and new approach, in: IEEE Conference on Parallel and Distrib. Processing, November, 1994.
15. Hu J.J., and Goodman E. The hierarchical fair competition (HFC) model for parallel evolutionary algorithms. Proceedings of the 2002 Congress on Evolutionary Computation, CEC2002 (2002), IEEE, Honolulu, Hawaii
16. W. Pedrycz, Granular computing: an introduction, in: IFSA World Congress and 20th NAFIPS International Conference, Joint 9th, 2001, vol. 3, pp. 1349-1354.
17. J.J. Hu, E.D. Goodman, K.S. Seo, M. Pei, Adaptive hierarchical fair competition (AHFC) model for parallel evolutionary algorithms, in: GECCO2002-Genetic and Evolutionary Computation, IEEE, Honolulu, Hawaii, 2002, pp. 772-779.
18. A. Oliveira, L. Lorena, A. Preto, S. Stephany, An adaptive hierarchical fair competition genetic algorithm for large-scale numerical optimization, in: SBRN 2004- Brazilian Symposium on Neural Networks, 2004.
19. Wang L.X., and Mendel J.M. Generating fuzzy rules from numerical data with applications. IEEE Trans. Syst., Man, Cybernet. 22 6 (1992) 1414-1427
20. Crowder III R.S. Predicting the Mackey-Glass time series with cascade-correlation learning. In: Touretzky D., Hinton G., and Sejnowski T. (Eds). Proceedings of the 1990 Connectionist Models Summer School (1990), Carnegic Mellon University 117-123
21. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst., Man, Cybernet. 23 3 (1993) 665-685
22. Maguire L.P., Roche B., McGinnity T.M., and McDaid L.J. Predicting a chaotic time series using a fuzzy neural network. Inform. Sci. 112 (1998) 125-136
23. Kukolj D. Design of adaptive Takagi-Sugeno-Kang fuzzy models. Appl. Soft Comput. 2 (2002) 89-103
24. Angelov P., and Buswell R. Identification of evolving fuzzy rule-based models. IEEE Trans. Fuzzy Syst. 10 5 (2002) 667-677
25. Song Q., and Kasabov N.K. NFI: a neuro-fuzzy inference method for transductive reasoning. IEEE Trans. Fuzzy Syst. 13 6 (2005)
26. Oh S.K., Pedrycz W., and Park H.S. Hybrid identification in fuzzy-neural networks. Fuzzy Sets Syst. 138 2 (2003) 399-426
27. Park H.S., and Oh S.K. Fuzzy relation-based fuzzy neural-networks using a hybrid identification algorithm. Int. J. Contr., Autom., Syst. 1 3 (2003) 289-300
28. Park H.S., and Oh S.K. Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation. Int. J. Cont., Autom., Syst. 1 2 (2003) 194-202
29. Oh S.K., Pedrycz W., and Park H.S. Implicit rule-based fuzzy-neural networks using the identification algorithm of hybrid scheme based on information granulation. Adv. Eng. Inform. 16 4 (2002) 247-263
30. Oh S.K., and Pedrycz W. A new approach to self-organizing multi-layer fuzzy polynomial neural networks based on genetic optimization. Adv. Eng. Inform. 18 (2004) 29-39
31. Sugeno M., and Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1 (1993) 7-31
32. Gomez-Skarmeta A.F., Delgado M., and Vila M.A. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets Syst. 106 (1999) 178-188
33. Kim E., Lee H., Park M., and Park M. A simply identified Sugeno-type fuzzy model via double clustering. Inform. Sci. 110 (1998) 25-39
34. Kim E.T., Park M.K., Ji S.H., and Park M. A new approach to fuzzy modeling. IEEE Trans. Fuzzy Syst. 5 (1997) 328-337
35. Tsekouras G.E. On the use of the weighted fuzzy c-means in fuzzy modeling. Adv. Eng. Software 36 (2005) 287-300
36. In: Krishnaiah P.R., and Kanal L.N. (Eds). Classification, pattern recognition, and reduction of dimensionality. Handbook of Statistics vol. 2 (1982), North-Holland, Amsterdam
37. Mackey M.C., and Glass L. Oscillation and chaos in physiological control systems. Science 197 (1977) 287-289
38. Park K.J., Oh S.K., and Kim H.K. Optimization of fuzzy set-fuzzy systems based on IG by means of GAs with successive tuning method. J. Elect. Eng. Technol. 3 1 (2008) 101-107