Typical hesitant fuzzy negations

International Journal of Intelligent Systems

Volumn 29, Issue 6, 2014, Pages 525-543

  Bedregal, Benjamin ^a   Santiago, Regivan H N ^a   Bustince, Humberto ^b   Paternain, Daniel ^b   Reiser, Renata ^c  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

^b PUBLIC UNIVERSITY OF NAVARRE   (Spain)

^c FEDERAL UNIVERSITY OF PELOTAS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY LOGIC;

FUZZY DEGREES; FUZZY NEGATIONS; HESITANT FUZZY SETS; MEMBERSHIP DEGREES; MEMBERSHIP VALUES; THEORETICAL APPROACH;

FUZZY SETS;

EID: 84898947224     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.21655     Document Type: Article

References (39)

1. Torra V, Narukawa Y,. On hesitant fuzzy sets and decision. In: Proc FUZZ-IEEE; Jeju Island, Korea 2009. pp 1378-1382.
2. Torra V,. Hesitant fuzzy sets. Int J Intell Syst 2010; 25: 529-539.
3. Farhadinia B,. A novel method of ranking hesitant fuzzy values for multiple attribute decision-making problems. Int J Intell Syst 2013; 28 (8): 752-767.
4. Wei G,. Hesitant fuzzy prioritized operators and their application to multiple attribute decision making. Int J Uncertain, Fuzzi Knowl-Based Syst 2012; 31: 176-182.
5. Xia M, Xu Z,. Hesitant fuzzy information aggregation in decision making. Int J Approx Reason 2011; 52 (3): 395-407.
6. Xia M, Xu Z, Chen N,. Some hesitant fuzzy aggregation operators with their application in group decision making. Group Decis Negot 2013; 22 (2): 259-279.
7. Bedregal B, Reiser RHS, Bustince H, Lopez-Molina C, Torra V,. Aggregation functions for typical hesitant fuzzy elements and the action of automorphisms. Inform Sci 2014; 255 (1): 82-99.
8. Zhu B, Xu Z, Xia M,. Hesitant fuzzy geometric Bonferroni means. Inform Sci 2012; 205 (1): 72-85.
9. Zadeh LA,. Fuzzy sets. Inform Control 1965; 8: 338-353.
10. Esteves F,. Negaciones en Retículos Completos. Stochastica 1975; 1: 49-66.
11. Lowen R,. On fuzzy complements. Inform Sci 1978; 14 (2): 107-113.
12. Yager RR,. On the measure of fuzziness and negation part I: membership in the unit interval. Int J General Syst 1979; 5: 221-229.
13. Trillas E,. Sobre funciones de negaciõn en la teoria de los conjuntos difusos. Stochastica 1979; 3 (1): 47-59.
14. Jun W, Shitong W, Chung F,. Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Machine Learning Cybernetics 2011; 2 (4): 261-271.
15. Lilly JH,. Evolution of a negative-rule fuzzy obstacle avoidance controller for an autonomous vehicle. IEEE Trans Fuzzy Syst 2007; 15 (4): 718-728. (Pubitemid 47316719)
16. Nguyen TM, Wu JQM,. A combination of positive and negative fuzzy rules for image classification problem. In: Proc 2008 7th Int Conf on Machine Learning and Applications; San Diego, California, USA. 2008. pp 741-746.
17. Pan Z, Wang C, Zhang L,. Three kinds of negations in fuzzy knowledge and their applications to decision making in financial investment. In: Computational collective intelligence. Technologies and applications, Lect Notes Comput Sci, 2010; 6422: 391-401.
18. Wang S, Pan Z, Yang L,. Fuzzy decision making based on fuzzy logic with contradictory negation, opposite negation and medium negation. In: Artificial intelligence and computational intelligence, Lect Notes Comput Sci, 2012; 7530: 200-208.
19. Bedregal BC,. On interval fuzzy negations. Fuzzy Sets Syst 2010; 161: 2290-2313.
20. Bedregal BC, Takahashi A,. Interval valued versions of T-conorms, fuzzy negations and fuzzy implications. In: Proc 2006 IEEE Int Conf on Fuzzy Systems, Vancouver, British Columbia, Canada 1981-1987. DOI: 10.1109/FUZZY.2006.1681975.
21. Deschrijver G, Cornelis C, Kerre EE,. On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Trans Fuzzy Syst 2004; 12 (1): 45-61.
22. Da Costa CG, Bedregal BC, Dõria Neto AD,. Relating De Morgan triples with Atanassov's intuitionistic De Morgan triples via automorphisms. Int J Approx Reason 2011; 52 (4): 473-487.
23. Bedregal B, Beliakov G, Bustince H, Calvo T, Mesiar R, Paternain D,. A class of fuzzy multisets with a fixed number of memberships. Inform Sci 2012; 189: 1-17.
24. Bedregal BC, Beliakov G, Bustince H, Fernández J, Pradera A, Reiser RHS,. Negations generated by bounded lattices t-norms. IPMU 3, Communications in Computer and Information Science, Vol 299. Springer; 2012. pp 326-335.
25. Palmeira ES, Bedregal B, Mesiar R,. A new way to extend t-norms, t-conorms and negations. Fuzzy Sets Syst. 2014; 240: 1-21.
26. Bonikowski Z, Wybraniec-Skardowska U,. Rough sets and vague sets. In:, Kryszkiewicz, M, et al., editors. RSEISP 2007. LNAI, Vol 4585. Berlin, Germany: Springer-Verlag; 2007. pp 122-132.
27. Baczyński M, Jayaram B,. Fuzzy implications. Berlin, Germany: Springer; 2008.
28. Xu Z, Xia M,. Distance and similarity measures for hesitant fuzzy sets. Inform Sci 2011; 181 (11): 2128-2138.
29. Xu Z, Xia M, On distance and correlation measures of hesitant fuzzy information. Int J Intell Syst, 2011; 26: 410-425.
30. Bedregal B, Santiago RHN,. Interval representations, Łukasiewicz implicators and Smets-Magrez axioms. Inform Sci 2013; 221: 192-200.
31. Klement EP, Mesiar R, Pap E,. Triangular norms. Dordrecht, The Netherlands: Kluwer; 2000.
32. Nguyen HT, Walker EA,. A first course in fuzzy logics. 2nd edn. Boca Raton, FL: CRC Press; 2000.
33. Wang YJ,. Fuzzy multi-criteria decision-making based on positive and negative extreme solutions. Appl Math Model 2011; 35: 1994-2004.
34. Xu Z, Hu H,. Projection models for intuitionistic fuzzy multiple attribute decision making. Int J Inform Technol Decision Making 2010; 9 (2): 267-280.
35. Bojadziev G, Bojadziev M,. Fuzzy logic for business, finance, and managenment. 2nd. edn. New Jersey: World Scientific Publishing; Singapore 2007.
36. Barrenechea E, Fernandez J, Pagola M, Chiclana F, Bustince H,. Construction of interval-valued fuzzy preference relations from ignorance functions and fuzzy preference relations. Application to decision making. Knowledge-Based Systems, 2014; 58: 33-44.
37. Paternain D, Jurio A, Barrenechea E, Bustince H, Bedregal B, Szmidt E,. An alternative to fuzzy methods in decision-making problems. Expert Syst Appl 2012; 39: 7729-7735.
38. Sanz J, Fernández A, Bustince H, Herrera F,. A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position. Int J Approx Reason 2011; 52 (6): 751-766.
39. Zhang Z,. Hesitant fuzzy power aggregation operators and their application to multiple attribute group decision making. Inform Sci 2013; 234: 150-181.