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 .
theory (Zadeh, 1965) is tool that can handle uncertainty and imprecision effortlessly.
Atanassov  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).
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.
theory, introduced by Zadeh , can be used to deal with these factors in the modelling of the systems.
At this time, the people should use hybrid intuitionistic fuzzy set
to make a decision.
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.
put forth a novel concept of soft rough fuzzy sets
by combining rough sets, soft sets, and fuzzy sets
and we call it Feng-soft rough fuzzy set