Sparse data in the evolutionary generation of fuzzy models

Fuzzy Sets and Systems

Volumn 138, Issue 2, 2003, Pages 363-379

  Spiegel, Daniel ^a   Sudkamp, Thomas ^b  

^a KUTZTOWN UNIVERSITY   (United States)

^b Wright State University   (United States)

Author keywords

Approximate reasoning; Fuzzy models; Genetic algorithms

Indexed keywords

APPROXIMATION THEORY; DATA ACQUISITION; EVOLUTIONARY ALGORITHMS; FUNCTIONS; LARGE SCALE SYSTEMS;

APPROXIMATE REASONING;

FUZZY CONTROL;

EID: 0043092358     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(03)00136-2     Document Type: Article

References (19)

1. T. Bäck, F. Kursawe, Evolutionary algorithms for fuzzy logic: a brief overview, in: Proc. 5th Int. Conf. IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems, vol. 2, 1994, pp. 659-664.
2. Bäck T., Schwefel H.P. An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1):1993;1-23.
3. O. Cordón, F. Herrera, Evolutionary design of TSK fuzzy rule-based systems using (λ, μ) -evolution strategies, in: Sixth IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), Barcelona, 1997, pp. 509-514.
4. Cordón O., Herrera F. A two-stage evolutionary process for designing TSK fuzzy rule-based systems. IEEE Trans. Systems Man Cybernet. B. 29(6):1999;703-715.
5. Cordón O., Herrera F., Hoffmann F., Magdalena L. Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Rule Bases. 2001;World Scientific Publishing, Singapore.
6. Cordón O., Herrera F., Lozano M. A classified review of the combination fuzzy-logic genetic algorithms bibliography. Sanchez E., Shibata T., Zadeh L. Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives. 1997;209-241 World Scientific Publishing, Singapore.
7. T. Furuhashi, K. Nakaoka, Y. Uchikawa, An efficient finding of fuzzy rules using a new approach to genetic based machine learning, in: Proc. 4th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE'95), Yokohama, March 1995, pp. 715-722.
8. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning. 1989;Addison-Wesley, Reading, MA.
9. J. Haslinger, U. Bodenhofer, M. Burger, Data-driven construction of Sugeno controllers: analytical aspects and new numerical methods, in: Proc. Joint 9th IFSA Congress and NAFIPS 20th Int. Conf., Vancouver, July 2001, pp. 239-244.
10. F. Herrera, J.L. Verdegay (Eds.), Genetic Algorithms and Soft Computing, Physica-Verlag, Berlin, 1996.
11. C.L. Karr, Design of an adaptive fuzzy logic controller using a genetic algorithm, in: Proc. 4th Int. Conf. on Genetic Algorithms, San Diego, CA, July 1991, pp. 450-457.
12. Karr C.L., Gentry E.J. Fuzzy control of pH using genetic algorithms. IEEE Trans. Fuzzy Systems. 1:1993;46-53.
13. W. Pedrycz (Ed.), Fuzzy Evolutionary Computation, Kluwer Academic Publishers, Boston, 1997.
14. Sanchez E., Shibata T., Zadeh L.A. Genetic Algorithms and Fuzzy Logic Systems Soft Computing Perspectives. 1997;World Scientific, Singapore.
15. D. Spiegel, T. Sudkamp, Evolutionary strategies for fuzzy models: local vs global construction, in: Proc. 1999 Conf. of the North American Fuzzy Information Processing Society, New York, 1999, pp. 203-207.
16. D. Spiegel, T. Sudkamp, Compensating for sparse data in evolutionary generation of fuzzy models, in: The 2000 IEEE Int. Conf. on Systems, Man, and Cybernetics, Atlanta, 2000, pp. 439-444.
17. Spiegel D., Sudkamp T. Employing locality in the evolutionary generation of fuzzy rule bases. IEEE Trans. Systems Man Cybernet. B. 32(3):2002;296-305.
18. Sudkamp T., Hammell II R.J. Interpolation, completion, and learning fuzzy rules. IEEE Trans. Systems Man Cybernet. 24(2):1994;332-342.
19. Sudkamp T., Hammell II R.J. Rule base completion in fuzzy models. Pedrycz W. Fuzzy Modeling: Paradigms and Practice. 1996;313-330 Kluwer Academic Publishers, Dordrecht, MA.