Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Studies in Fuzziness and Soft Computing |
| Publisher | Springer Verlag |
| Pages | 231-269 |
| Number of pages | 39 |
| Volume | 363 |
| DOIs | |
| State | Published - Jan 1 2018 |
Publication series
| Name | Studies in Fuzziness and Soft Computing |
|---|---|
| Volume | 363 |
| ISSN (Print) | 1434-9922 |
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All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Computational Mathematics
Cite this
Interval-valued fuzzy graphs. / Mathew, Sunil; Mordeson, John N.; Malik, Davender S.
Studies in Fuzziness and Soft Computing. Vol. 363 Springer Verlag, 2018. p. 231-269 (Studies in Fuzziness and Soft Computing; Vol. 363).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
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 -