First, we propose a modified non-membership function to generate intuitionistic
fuzzy set, which highlights the effect of uncertainty and makes good use of image information.
To approximate those uncertainties exists in the given linguistics words the
fuzzy set theory is introduced by Zadeh [10].
Atanassov [1] extends the
fuzzy set characterized by a membership function to the intuitionistic
fuzzy set (IFS), which is characterized by a membership function, a non-membership function, and a hesitancy function.
An intuitionistic
fuzzy set offers a better way to deal with uncertain multi-attribute problems (Mehlawat & Grover, 2018; Rodriguez, Ortega, & Concepcion, 2017; Ren, Xu, & Wang, 2017; Khemiri, Elbedouimaktouf, Grabot, & Zouari, 2017; Ye, 2017).
Fuzzy set theory in fuzzy decision making processes was first introduced by Bellman and Zadeh (1970).
In
fuzzy set theory, the measurement of the degree of fuzziness in
fuzzy sets and other extended higher order
fuzzy sets is an important concept in dealing with real world problems.
The purpose of this paper is to present an uncertainty management model that applies
fuzzy set theory to these indicators.
Fuzzy set theory, introduced by Zadeh [1], can be used to deal with these factors in the modelling of the systems.
For a nonlinear engineering problem,
fuzzy set theory is very helpful, and a tool that transforms this linguistic control strategy into a mathematical control method in modeling complex and vague systems.