Interval fuzzy modeling applied to Wiener models with uncertainties

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 35, Issue 5, 2005, Pages 1092-1095

  Škrjanc, Igor ^a   Blažič, Sašo ^a   Agamennoni, Osvaldo Enrique ^b  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSIDAD NACIONAL DEL SUR   (Argentina)

Author keywords

Interval fuzzy model; Linear programming; Min max optimization; Wiener model

Indexed keywords

IDENTIFICATION (CONTROL SYSTEMS); LINEAR PROGRAMMING; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION; VECTORS;

INTERVAL FUZZY MODEL; MIN-MAX OPTIMIZATION; WIENER MODEL;

FUZZY SETS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; EVALUATION; FUZZY LOGIC; INFORMATION RETRIEVAL; LETTER; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FUZZY LOGIC; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 26844431514     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2005.850166     Document Type: Article

References (17)

1. H. Tanaka, S. Uejima, and K. Asai, "Linear regression analysis with fuzzy model," IEEE Trans. Syst., Man, Cybern., vol. SMC-12, no. 6, pp. 903-907, 1982.
2. H. Tanaka and J. Watada, "Possibilistic linear systems and their applications to the linear regression model," Fuzzy Sets Syst., vol. 27, pp. 275-289, 1988.
3. G. Peters, "Fuzzy linear regression with fuzzy intervals," Fuzzy Sets Syst., vol. 63, pp. 45-55, 1994.
4. Y. S. Chen, "Outliers detection and confidence interval modification in fuzzy regression," Fuzzy Sets Syst., vol. 119, no. 2, pp. 259-272, 2001.
5. B. Turksen, "Type I and type II fuzzy system modeling," Fuzzy Sets Syst., vol. 106, pp. 11-34, 1999.
6. N. Karnik and J. Mendel, "Operation on type-2 fuzzy sets," Fuzzy Sets Syst., vol. 122, no. 2, pp. 327-348, 2001.
7. Q. Liang and J. Mendel, "Interval type-2 fuzzy logic systems: Theory and design," IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 535-550, Oct. 2000.
8. B. Kosko, "Fuzzy systems as universal approximators," IEEE Trans. Comput., vol. 43, no. 11, pp. 1329-1333, Nov. 1994.
9. H. G. C. Ying, "Necessary conditions for some typical fuzzy systems as universal approximators," Automatica, vol. 33, pp. 1333-1338, 1997.
10. L.-X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 807-814, Sep. 1992.
11. R. R. Goldberg, Methods of Real Analysis. NewYork: Wiley, 1976.
12. C.-H. Lin, "Siso nonlinear system identification using a fuzzy-neural hybrid system," Int. J. Neural Syst., vol. 8, no. 3, pp. 325-337, 1997.
13. P. Julian, M. Jordan, and A. Desages, "Canonical piecewise linear approximation of smooh functions," IEEE Trans. Circ. Syst., vol. 45, no. 5, pp. 567-571, May 1998.
14. S. Gerkšič, D. Juričič, S. Strmčnik, and D. Matko, "Wiener model based nonlinear predictive control," Int. J. Syst. Sci., vol. 31, no. 2, pp. 189-202, 2000.
15. J. C. Bezdek, C. Coray, R. Gunderson, and J. Watson, "Detection and characterization of cluster substructure, I. Linear structure: Fuzzy c-lines," SIAM J. Appl. Math., vol. 40, no. 2, pp. 339-357, 1981.
16. R. J. Hathaway, C. J. Bezdek, and Y. K. Hu, "Generalized fuzzy c-means clustering strategies using Lp norm distances," IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 576-582, Oct. 2000.
17. L. Ljung, System Identification. Englewood Cliffs, NJ: Prentice-Hall, 1987.