TY - CHAP

T1 - Interval-valued fuzzy graphs

AU - Mathew, Sunil

AU - Mordeson, John N.

AU - Malik, Davender S.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The results in this chapter are based mostly on the works in Akram (Inf Sci, 181:5548–5564, 2011) [5], Akram and Dudek (Comput Math Appl, 61(2):289–299, 2011) [14], Akram et al. (J Appl Math, 2013, 2013) [19], Akram et al. (Afr math, 2014) [23]. In 1975, Zadeh (Inf Sci, 8:199–249, 1975) [194] introduced the notion of interval-valued fuzzy sets as an extension of fuzzy sets (Zadeh, Inf Control, 8:338–353, 1965, [190]) in which the values of the memberships degrees are intervals in [0, 1] instead of elements in [0, 1]. Interval-valued fuzzy sets provide a more adequate description of uncertainty than traditional fuzzy sets in some cases. It can therefore be important to use interval-valued fuzzy sets in applications, e.g., in fuzzy control.

AB - The results in this chapter are based mostly on the works in Akram (Inf Sci, 181:5548–5564, 2011) [5], Akram and Dudek (Comput Math Appl, 61(2):289–299, 2011) [14], Akram et al. (J Appl Math, 2013, 2013) [19], Akram et al. (Afr math, 2014) [23]. In 1975, Zadeh (Inf Sci, 8:199–249, 1975) [194] introduced the notion of interval-valued fuzzy sets as an extension of fuzzy sets (Zadeh, Inf Control, 8:338–353, 1965, [190]) in which the values of the memberships degrees are intervals in [0, 1] instead of elements in [0, 1]. Interval-valued fuzzy sets provide a more adequate description of uncertainty than traditional fuzzy sets in some cases. It can therefore be important to use interval-valued fuzzy sets in applications, e.g., in fuzzy control.

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U2 - 10.1007/978-3-319-71407-3_7

DO - 10.1007/978-3-319-71407-3_7

M3 - Chapter

AN - SCOPUS:85039989432

VL - 363

T3 - Studies in Fuzziness and Soft Computing

SP - 231

EP - 269

BT - Studies in Fuzziness and Soft Computing

PB - Springer Verlag

ER -