TY - CHAP
T1 - Optimization under fuzzy max-t-norm relation constraints
AU - Lāma, Reinis
AU - Asmuss, Svetlana
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Fuzzy relation equations and inequalities play an important role in many tools of fuzzy modelling and have been extensively studied. In many practical applications they are used as constraints in optimization. Algorithms for specific objective functions have been proposed by many authors. In this paper we introduce a method to convert a system of fuzzy relation constraints with max-t-norm composition to a linear constraint system by adding integer variables. A numerical example is provided to illustrate the proposed method.
AB - Fuzzy relation equations and inequalities play an important role in many tools of fuzzy modelling and have been extensively studied. In many practical applications they are used as constraints in optimization. Algorithms for specific objective functions have been proposed by many authors. In this paper we introduce a method to convert a system of fuzzy relation constraints with max-t-norm composition to a linear constraint system by adding integer variables. A numerical example is provided to illustrate the proposed method.
KW - Fuzzy relation constraints
KW - Max-t-norm constraints
KW - Optimization under fuzzy relation constraints
UR - https://link.springer.com/chapter/10.1007/978-3-030-16024-1_17
UR - https://www.scopus.com/pages/publications/85066150001
U2 - 10.1007/978-3-030-16024-1_17
DO - 10.1007/978-3-030-16024-1_17
M3 - Chapter
SN - 9783030160241
VL - 819
T3 - Studies in Computational Intelligence
SP - 131
EP - 137
BT - Studies in Computational Intelligence
PB - Springer
CY - Cham
ER -