Fuzzy relation equations theory as a basis of fuzzy modelling: an overview
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Design of fuzzy logic controllers based on generalized T-operators
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
s-t fuzzy relational equations
Fuzzy Sets and Systems
Solving fuzzy relation equations with a linear objective function
Fuzzy Sets and Systems
Solving nonlinear optimization problems with fuzzy relation equation constraints
Fuzzy Sets and Systems
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Multi-objective optimization problems with fuzzy relation equation constraints
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition
Fuzzy Optimization and Decision Making
Solution Sets of Interval-Valued Min-S-Norm Fuzzy Relational Equations
Fuzzy Optimization and Decision Making
On the resolution and optimization of a system of fuzzy relational equations with sup-T composition
Fuzzy Optimization and Decision Making
A survey on fuzzy relational equations, part I: classification and solvability
Fuzzy Optimization and Decision Making
On the relation between equations with max-product composition and the covering problem
Fuzzy Sets and Systems
An efficient solution procedure for fuzzy relation equations with max-product composition
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.98 |
In this paper, an optimization model for minimizing an objective function with single-term exponents subject to fuzzy relational equations specified in max-product composition is presented. The solution set of such a fuzzy relational equation is a non-convex set. First, we present some properties for the optimization problem under the assumptions of both negative and nonnegative exponents in the objective function. Second, an efficient procedure is developed to find an optimal solution without looking for all the potential minimal solutions and without using the value matrix. An example is provided to illustrate the procedure.