Identification of general and double aggregation operators using monotone smoothing

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 937-942

  Beliakov, Gleb ^a   Calvo, Tomasa ^b  

^a DEAKIN UNIVERSITY   (Australia)

^b UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Aggregation operators; Empirical fit; Monotone approximation

Indexed keywords

AGGREGATION OPERATOR; AGGREGATION PROCESS; EMPIRICAL DATA; EMPIRICAL FIT; LIPSCHITZ; MONOTONE APPROXIMATION; MULTI-STEP;

AGGLOMERATION; FUZZY LOGIC; FUZZY SETS;

MATHEMATICAL OPERATORS;

EID: 33847287429     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. D. Dubois and H. Prade. A review of fuzzy set aggregation connectives. Information Sciences, 36:85-121, 1985.
2. T. Calvo, A. Kolesarova, M. Komornikova, and R. Mesiar. Aggregation operators: properties, classes and construction methods. In T. Calvo, G. Mayor, R. Mesiar, eds, Aggregation Operators. New Trends and Applications, pp. 3-104. Physica-Verlag, Heidelberg, New York, 2002.
3. T. Calvo and A. Pradera. Double aggregaion operators. Fuzzy Sets and Systems, 142:15-33, 2004.
4. H.-J. Zimmermann and P. Zysno. Latent connectives in human decision making. Fuzzy Sets and Systems, 4:37-51, 1980.
5. D. Filev and R. Yager. On the issue of obtaining OWA operator weights. Fuzzy Sets and Systems, 94:157-169, 1998.
6. H. Dyckhoff and W. Pedrycz. Generalized means as model of compensative connectives. Fuzzy Sets and Systems, 14:143-154, 1984.
7. G Beliakov. How to build aggregation operators from data? Int. J. Intelligent Systems, 18:903-923, 2003.
8. G. Beliakov. Monotone approximation of aggregation operators using least squares splines. Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 10:659-676, 2002.
9. G. Beliakov, R. Mesiar, and L. Valaskova. Fitting generated aggregation operators to empirical data. Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 12:219-236, 2004.
10. G. Beliakov. Identification of general aggregation operators by Lipschitz approximation. In M.H. Hamza, editor, The IASTED Intl. Conf. on AI and Appl., pp. 230-233, Innsbruck, Austria, 2005. ACTA Press.
11. G. Beliakov. Fitting triangular norms to empirical data. In P.E. Klement, R. Mesiar, eds, Logical, Algebraic, Analytic, and Probabilistic Aspects of Triangular Norms, pp. 255-265. Elsevier, New York, 2005