Improving the interpretability of data-driven evolving fuzzy-systems

Proceedings - 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications, EUSFLAT-LFA 2005 Joint Conference

Volumn , Issue , 2005, Pages 28-33

  Lughofer, Edwin ^a   Hüllermeier, Eyke ^b   Klement, Erich Peter ^a  

^a JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Complexity reduction; Evolving fuzzy systems; Incremental learning

Indexed keywords

COMPLEXITY REDUCTION; EVOLVING FUZZY SYSTEMS; HIGH DIMENSIONAL DATA; INCREMENTAL LEARNING; INTERPRETABILITY; ONLINE MODES; TAKAGI SUGENO FUZZY SYSTEMS;

FUZZY SYSTEMS;

FUZZY LOGIC;

EID: 84871941909     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. R. Babuska. Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston, 1998.
2. J. Espinosa and J. Vandewalle. Constructing fuzzy models with linguistic intergrity from numerical data-afreli algorithm. IEEE Transactions on Fuzzy Systems, 8:591-600, 2000.
3. W. Groißbock, E. Lughofer, and E.P. Klement. A comparison of variable selection methods with the main focus on orthogonalization. In Proceedings of SMPS Conference 2004, Oviedo, Spain, 2004.
4. L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, Prentic Hall Inc., Upper Saddle River, New Jersey 07458, 1999.
5. E. Lughofer. Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models. PhD thesis, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, February 2005.
6. E. Lughofer and E.P. Klement. FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems. In to appear in Proceedings of FUZZ-IEEE 2005, Reno, Nevada, U.S.A., 2005.
7. J. Valente De Oliveira. Semantic constraints for membership function optimization. IEEE Transactions on Systems, Man and Cybernetics-part A: Systems and Humans, 29(1):128-138, 1999.
8. M. Setnes. Simplification and reduction of fuzzy rules. In J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, editors, Interpretability Issues in Fuzzy Modeling, volume 128 of Studies in Fuzziness and Soft Computing, pages 278-302. Springer, Berlin, 2003.
9. M. Setnes, R. Babuska, U. Kaymak, and H.R.v.N. Lemke. Similarity measures in fuzzy rule base simplification. IEEE Trans. SMC-B, 28:376-386, 1998.
10. T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15(1):116-132, 1985.
11. L.X. Wang. Fuzzy systems are universal approximators. In Proceedings of the IEEE International Conference on Fuzzy Systems, pages 1163-1169, 1992.