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Setnes, M.; Babuska, R.; Verbruggen, H.B. (1998)
Publisher: IEEE
Languages: English
Types: Article
Subjects:
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] W. Pedrycz, Fuzzy Sets Engineering. Boca Raton, FL: CRC, 1995.
    • [2] B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: PrenticeHall, 1992.
    • [3] J. H. Nie and T. H. Lee, “Rule-based modeling: Fast construction and optimal manipulation,” IEEE Trans. Syst., Man, Cybern. A, vol. 26, pp. 728-738, Nov. 1996.
    • [4] R. Babusˇka and H. B. Verbruggen, “Fuzzy set methods for local modeling and identification,” in Multiple Model Approaches to Nonlinear Modeling and Control, R. Murray-Smith and T. A. Johansen, Eds. London, U.K.: Taylor & Francis, 1997, pp. 75-100.
    • [5] L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1994.
    • [6] C. T. Chao, Y. J. Chen, and T. T. Teng, “Simplification of fuzzy-neural systems using similarity analysis,” IEEE Trans. Syst., Man, Cybern. B, vol. 26, pp. 344-354, Apr. 1996.
    • [7] M. Setnes, R. Babusˇka, U. Kaymak, and H. R. van Nauta Lemke, “Similarity measures in fuzzy rule base simplification,” IEEE Trans. Syst., Man, Cybern. B, to be published.
    • [8] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Syst., Man, Cybern., vol. SMC-1, pp. 28-44, Jan. 1973.
    • [9] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. SMC-15, pp. 116-132, Feb. 1985.
    • [10] R. Babusˇka and H. B. Verbruggen, “Identification of composite linear models via fuzzy clustering,” in Proc. European Contr. Conf., Rome, Italy, Sept. 1995, pp. 1207-1212.
    • [11] D. E. Gustafson and W. C. Kessel, “Fuzzy clustering with a fuzzy covariance matrix,” in Proc. IEEE CDC, San Diego, CA, pp. 761-766, 1979.
    • [12] I. Gath and A. B. Geva, “Unsupervised optimal fuzzy clustering,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 773-781, July 1989.
    • [13] U. Kaymak and R. Babusˇka, “Compatible cluster merging for fuzzy modeling,” in Proc. FUZZ-IEEE/IFES'95, Yokohama, Japan, pp. 897-904.
    • [14] R. Kruse, J. Gebhardt, and F. Klawonn, Foundations of Fuzzy Systems, New York: Wiley, 1994.
    • [15] R. Krishnapuram and C.-P. Freg, “Fitting an unknown number of lines and planes to image data through compatible cluster merging,” Pattern Recognit., vol. 4, no. 25, pp. 385-400, 1992.
    • [16] M. Setnes, R. Babusˇka, H. B. Verbruggen, M. D. Sa´nchez, and H. F. P. van den Boogaard, “Fuzzy modeling and similarity analysis applied to ecological data,” in Proc. FUZZ-IEEE'97, Barcelona, Spain, pp. 415-420.
    • [17] R. Babusˇka, H. J. L. van Can, and H. B. Verbruggen, “Fuzzy modeling of enzymatic Penicillin-G conversion,” in 13th IFAC World Congr., Preprints, vol. N. San Francisco, CA, July 1996, pp. 479-484.
    • [18] N. W. Bormans, “Fuzzy sets in finance and insurance,” M.Sc. thesis, Dept. Elec. Eng., Contr. Lab., Delft Univ. of Technol., Delft, The Netherlands, Nov. 1996.
    • [19] R. Babusˇka, H. A. B. te Braake, A. J. Krijgsman, and H. B. Verbruggen, “Comparison of intelligent control schemes for real-time pressure control,” Contr. Eng. Practice, vol. 4, no. 11, pp. 1585-1592, 1996.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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