[1] Beliakov, G., Pradera, A., Calvo, T.:
Aggregation Functions: A guide for practitioners. Studies in Fuzziness and Soft Computing 221 (2007), 261-269.
DOI 10.1007/978-3-540-73721-6_5
[2] Bustince, H., Barrenechea, E., Calvo, T., James, S., Beliakov, G.:
Consensus in multi-expert decision making problems using penalty functions defined over a Cartesian product of lattices. Inform. Fusion 17 (2014), 56-64.
DOI 10.1016/j.inffus.2011.10.002
[4] Bustince, H., Galar, M., Bedregal, B., Kolesárová, A., Mesiar, R.:
A new approach to interval-valued Choquet integrals and the problem of ordering in interval-valued fuzzy set applications. IEEE Trans. Fuzzy Systems 21 (2013), 1150-1162.
DOI 10.1109/tfuzz.2013.2265090
[7] Dubois, D., Kerre, E., Mesiar, R., Prade, H.:
Fuzzy interval analysis. In: Fundamentals of Fuzzy Sets (D. Dubois and H. Prade, eds.), The Handbooks of Fuzzy Sets Series, Vol. 7, Springer US 2000, pp. 483-581.
DOI 10.1007/978-1-4615-4429-6_11 |
MR 1890240 |
Zbl 0988.26020
[10] Fortin, J., Dubois, D., Fargier, H.:
Gradual numbers and their application to fuzzy interval analysis. IEEE Trans. Fuzzy Systems 16 (2008), 388-402.
DOI 10.1109/tfuzz.2006.890680
[12] Herrera, F., Martínez, L.:
A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making. IEEE Trans. Systems Man and Cybernetics, Part B: Cybernetics 31 (2001), 227-234.
DOI 10.1109/3477.915345
[14] Kosiński, W., Prokopowicz, P., Rosa, A.:
Defuzzification functionals of ordered fuzzy numbers. IEEE Trans. Fuzzy Systems 21 (2013), 1163-1169.
DOI 10.1109/tfuzz.2013.2243456
[16] Lodwick, W. A., Untiedt, E. A.:
A comparison of interval analysis using constraint interval arithmetic and fuzzy interval analysis using gradual numbers. In: Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008, pp. 1-6.
DOI 10.1109/nafips.2008.4531302
[17] Martin, T. P., Azvine, B.:
The X-mu approach: Fuzzy quantities, fuzzy arithmetic and fuzzy association rules. In: IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2013, pp. 24-29.
DOI 10.1109/foci.2013.6602451
[18] Melin, P., Castillo, O.:
A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Applied Soft Computing J. 21 (2014), 568-577.
DOI 10.1016/j.asoc.2014.04.017
[19] Mesiar, R., Kolesárová, A., Calvo, T., Komorníková, M.:
A review of aggregation functions. In: Fuzzy Sets and Their Extensions: Representation, Aggregation and Models (H. Bustince et al., eds.), Springer, Berlin 2008, pp. 121-144.
DOI 10.1007/978-3-540-73723-0_7 |
Zbl 1147.68081
[23] Ochoa, G., Lizasoain, I., Paternain, D., Bustince, H., Pal, N. R.:
Some properties of lattice OWA operators and their importance in image processing. In: Proc. IFSA-EUSFLAT 2015, pp. 1261-1265.
DOI 10.2991/ifsa-eusflat-15.2015.178
[30] Yager, R. R.:
On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems Man and Cybernetics 18 (1988), 183-190.
DOI 10.1109/21.87068 |
MR 0931863 |
Zbl 0637.90057
[31] Zhou, S. M., Chiclana, F., John, R. I., Garibaldi, J. M.:
Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers. Fuzzy Sets and Systems 159 (2008), 3281-3296.
DOI 10.1016/j.fss.2008.06.018 |
MR 2467606 |
Zbl 1187.68619
[32] Zhou, S. M., Chiclana, F., John, R. I., Garibaldi, J. M.:
Alpha-level aggregation: A practical approach to type-1 OWA operation for aggregating uncertain information with applications to breast cancer treatments. IEEE Tran. Knowledge Data Engrg. 23 (2011), 1455-1468.
DOI 10.1109/tkde.2010.191