## 1 Introduction  
Fuzzy set theory was first introduced by [PERSON] in the 1960s as a way to capture the uncertainty and ambiguity often overlooked in complex systems. It can be considered as a generalization of the theory of classical sets. As research into fuzzy logic continues, fuzzy sets (FS) are gradually being refined to better reflect the original idea of modeling linguistic variables, which builds on the pillars of inaccuracy, vagueness and ambiguity that arise in language variables. This resulted in three main streams of fuzzy set representation: (a) type-1 fuzzy set (T1 FS); (b) general type-2 fuzzy sets (GT2 FS); (c) interval type-2 fuzzy sets (IT2 FS). However, the computations with type-2 fuzzy sets are complex [1]. To solve this, simplification of type-2 is conducted through a special kind of type-2 fuzzy set called interval type-2 fuzzy set. Interval type-2 fuzzy sets manage uncertainty in membership functions, but computations are less intense, keeping the capabilities to process uncertainties [2, 3]. The first mentioned fuzzy sets are considered to be a very simplified form of representation of linguistic variables, which is able to integrate exclusively a certain degree of uncertainty according to [4, 5], while type-2 interval fuzzy sets are able to integrate uncertainty in the form of intervals, thus not limiting the level of uncertainty. Although IT2 FS are more computationally intensive and more complex to design, according to [6], these sets are better able to deal with the additional noise and the nonlinear and chaotic environment of input data from various disciplines. The reason for the widespread use and ever-increasing popularity of fuzzy logic in many scientific fields lies in the fact that it is able to better understand human thinking and, above all, is able to process uncertain, ambiguous and incomplete data. The selection and setting of the correct membership functions and the determination of the relevant fuzzy rules on the basis of which the model provides outputs is absolutely essential for the correct and accurate functionality of the fuzzy model. While setting fuzzy rules can be solved using a team of experts or through neural networks, such as the ANFIS model, the construction of fuzzy membership functions and the adequate setting of their parameters is a demanding task and prone to errors [7]. Thanks to the possibility of the parameterization of standard and widely used fuzzy functions (for example triangular, trapezoidal or Gaussian) it is possible to directly and consistently compare the performance of these functions in type-1 and type-2 fuzzy models. As described by [6], in addition to these standard functions, less common fuzzy functions can be found; however, these can also be easily converted from type-1 to type-2 by parameterization. As [8] point out, the choice of the correct fuzzy function, including the appropriate setting of parameters, thus plays a fundamental and indispensable role in the design, implementation, performance and subsequent interpretation of the fuzzy model. The authors add that these parameters of fuzzy functions are possibilistic in nature and depend on the specific area or domain in which the model is applied. Expert research or other sophisticated methods that set the parameters are necessary for the correct selection and setting of fuzzy functions. Study in [9] emphasizes that the correct choice of fuzzy functions depends to a large and not negligible extent on the subjective perception of inaccurate or vague data. In other words, there are no criteria or consensus to determine the appropriate fuzzy functions for a particular area, although, as [10] points out, these functions play a central role in the high performance of the fuzzy model. Thus, fuzzy functions can be considered as building blocks of fuzzy set theory. However, fuzzy functions must satisfy the necessary condition that they must be in the range 0 to 1. If this condition is met, fuzzy functions can take various shapes and forms.