[1] O. Castillo and P. Melin, A review on interval type-2 fuzzy logic applications in intelligent
control, Information Sciences, 279 (2014), 615–631.
[2] C. Cornelis, G. Deschrijver and E. E. Kerre, Implication in intuitionistic and interval-valued
fuzzy set theory: construction, classification and application, International Journal of Approximate
Reasoning, 35 (2004), 55–95.
[3] S. Coupland and R. John, A fast geometric method for defuzzification of type-2 fuzzy sets,
IEEE Transaction on Fuzzy Systems, 16(4) (2008), 929–941.
[4] T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu and I. B. T¨urksen, Industrial applications
of type-2 fuzzy sets and systems: a concise review, Computer in Industry, 62 (2011), 125–137.
[5] G. Deschrijver, C. Cornelis and E. E. Kerre, On the representation of intuitionistic fuzzy
t-norms and t-conorms, IEEE Transaction on Fuzzy Systems, 12(1) (2004), 45–61.
[6] G. Deschrijver and E. E. Kerre, Classes of intuitionistic fuzzy t-norms satisfying the residuation
principle, International Journal of Uncertainty Fuzziness Knowledge-Based Systems,
11(6) (2003), 691–709.
[7] G. Deschrijver and E. E. Kerre, On the relationship between some extensions of fuzzy set
theory, Fuzzy Sets and Systems, 133 (2003), 227–235.
[8] A. Doostparast, M. H. Fazel Zarandi and H. Zakeri, On type-reduction of type-2 fuzzy sets:
A review, Applied Soft Computing, 27 (2015), 614–627.
[9] D. Dubois, On ignorance and contradiction considered as truth-values, Logic Journal of the
IGPL, 16(2) (2008), 195–216.
[10] B. V. Gasse, C. Cornelis, G. Deschrijver and E. E. Kerre, Triangle algebras: A formal logic
approach to interval-valued residuated lattices, Fuzzy Sets and Systems, 159 (2008), 1042–
1060.
[11] M. B. Gorza lczany, A method of inference in approximate reasoning based on interval-valued
fuzzy sets, Fuzzy Sets and Systems, 21(1) (1987), 1–17.
[12] M. B. Gorza lczany, An interval-valued fuzzy inference method-Some basic properties, Fuzzy
Sets and Systems, 31(2) (1989), 243–251.
[13] S. Greenfield, F. Chiclana, R. I. John and S. Coupland, The sampling method of defuzzification
for type-2 fuzzy sets: experimental evaluation, Information Sciences, 189 (2012),
77–92.
[14] H. Hagras, A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,
IEEE Transaction on Fuzzy Systems, 12(4) (2004), 524–539
[15] H. R. Hassanzadeh, M. T. A. Akbarzadeh and A. Rezaei, An interval-valued fuzzy controller
for complex dynamical systems with application to a 3-PSP parallel robot, Fuzzy Sets and
Systems, 235(16) (2014), 83–100.
[16] M. Y. Hsiao, T. S. Li, J. Z. Lee, C. H. Chao and S. H. Tsai, Design of interval type-2 fuzzy
sliding-mode controller, Information Sciences, 178(6) (2008), 1696–1716.
[17] C. F. Juang and Y. W. Tsao, A type-2 self-organizing neural fuzzy system and its FPGA implementation,
IEEE Transaction on System Man Cybernet. Part B: Cybernet, 38(6) (2008),
1537–1548.
[18] H. K. Lam, H. Li, C. Deters, E. L. Secco, H. A. Wurdemann and K. Althoefer, Control design
for interval type-2 fuzzy systems under imperfect premise matching, IEEE Transactions on
Industrial Electronics, 61(2) (2014), 956–968, art. no. 6480840.
[19] D. C. Li, Y. M. Li and Y. J. Xie, Robustness of interval-valued fuzzy inference, Information
Science, 181 (2011), 4754–4764.
[20] Y. M. Li and Y. J. Du, Indirect adaptive fuzzy observer and controller design based on interval
type-2 T-S fuzzy model, Applied Mathematical Modelling, 36(4) (2012), 1558–1569.
[21] Y. M. Li, Z. K. Shi and Z. H. Li, Approximation theory of fuzzy systems based upon genuine
many-valued implications: SISO cases, Fuzzy Sets and Systems, 130 (2002), 147–157.
[22] Y. M. Li, Z. K. Shi and Z. H. Li, Approximation theory of fuzzy systems based upon genuine
many-valued implications: MIMO cases, Fuzzy Sets and Systems, 130 (2002), 159–174.
[23] Q. Liang and J. M. Mendel, Interval type-2 fuzzy logic systems: theory and design, IEEE
Transaction on Fuzzy Systems, 8 (2000), 535–550.
[24] O. Linda and M. Manic, Uncertainty-robust design of interval type-2 fuzzy logic controller
for delta parallel robot, IEEE Trans. Ind. Inf. 7(4) (2011), 661–670.
[25] X. Liu and J. Mendel, Connect Karnik-Mendel algorithms to root-finding for computing the
centroid of an interval type-2 fuzzy set, IEEE Transaction on Fuzzy Systems, 19(4) (2011),
652–665.
[26] S. Mandal and B. Jayaram, SISO fuzzy relational inference systems based on fuzzy implications
are universal approximators, Fuzzy Sets and Systems, 277 (2015), 1–21.
[27] V. Nov´ak and S. Lehmke, Logical structure of fuzzy IF-THEN rules, Fuzzy Sets and Systems,
157(15) (2006), 2003–2029.
[28] V. Nov´ak, I. Perfilieva and J. Mˇckˇcr, Mathematical Principles of Fuzzy Logic, Kluwer Academic
Publishers, Boston, 1999.
[29] I. Perfilieva, Normal forms in BL-algebra off unctions and their contribution to universal
approximation, Fuzzy Sets and Systems, 143(1) (2004), 111–127.
[30] I. Perfilieva and V. Kreinovich, A new universal approximation result for fuzzy systems,
which reflects CNF-DNF duality, Int. J. Intell. Syst. 17(12) (2002), 1121–1130.
[31] Y. M. Tang and X. P. Liu, Differently implicational universal triple I method of (1, 2, 2)
type, Computers and Mathematics with Applications, 59(6) (2010), 1965–1984.
[32] I. B. T¨urksen, Type 2 representation and reasoning for CWW, Fuzzy Sets and Systems, 127
(2002), 17–36.
[33] I. B. T¨urksen and Y. Tian, Interval-valued fuzzy sets representation on multiple antecedent
fuzzy S-implications and reasoning, Fuzzy Sets and Systems, 52(2) (1992), 143–167.
[34] G. Wang and X. Li, Correlation and information energy of interval-valued fuzzy numbers,
Fuzzy Sets and Systtem, 103(1) (1999), 169–175.
[35] D. Wu, On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers,
IEEE Transactions on Fuzzy Systems, art. no. 6145645, 20(5) (2012), 832–848.
[36] D. Wu and W. W. Tan, A type-2 fuzzy logic controller for the liquid-level process, in: 2004
IEEE International Conference on Fuzzy Systems, (2004), Proceedings. 2 (2004), 953–958.
[37] H. Ying, Sufficient conditions on general fuzzy systems as function approximators, Automatic,
30(3) (1994), 521–525.
[38] H. Ying, General interval type-2 Mamdani fuzzy systems are universal approximators, Proceedings
of North American Fuzzy Information Processing Society Conference, New York,
NY, May 19–22, 2008.
[39] H. Ying, Interval type-2 Takagi-Sugeno fuzzy systems with linear rule consequent are universal
approximators, The 28th North American Fuzzy Information Processing Society Annual
Conference, Cincinnati, Ohio, June 14–17, 2009.
[40] L. A. Zadeh, The concepts of a linguistic variable and its application to approximate reasoning
(I), (II), Information Science, 8 (1975), 199–249; 301–357.
[41] L. A. Zadeh, The concepts of a linguistic variable and its application to approximate reasoning
(III), Information Science, 9 (1975), 43–80.
[42] W. Y. Zeng and S. Feng, Approximate reasoning algorithm of interval-valued fuzzy sets based
on least square method, Information Sciences, 272 (2014), 73–83.
[43] H. Zhou and H. Ying, A method for deriving the analytical structure of a broad class of
typical interval type-2 mamdani fuzzy controllers, in: IEEE Transactions on Fuzzy Systems,
art. no. 6341818, 21(3) (2013), 447–458.