## 3 Methodology
### Model Construction
#### 3.3.1 Fuzzy Logic Background and Modeling  
Despite classical logic, which defines truth values as either true or false, fuzzy logic uses a more complex strategy to simulate logical thinking. Individuals form agreements with one another using language data. By utilizing fuzzy logic in the modeling and computation processes, linguistic ambiguities in everyday spoken language can be taken into consideration [99]. Fuzzy systems are mostly used in the domain of how solutions would be thought of in the event that such knowledge existed. Any difficulties are attempted to be roughly modeled, and FL uses advanced mathematical answers to regulate the problem [100]. By applying language labels stimulated by membership functions, fuzzy set theory may perform numerical computations and offer a systematic calculus associated with such linguistic information. Designing a fuzzy inference system--a function that converts a set of inputs into outputs using human-understandable rules as opposed to more abstract mathematical formulas--is made possible by fuzzy logic. The real procedure of mapping a given set of input variables to an output that is dependent on a set of fuzzy rules is known as fuzzy inference. Fuzzification, fuzzy rule base, fuzzy inference engine, and defuzzification are the four basic processes of a general fuzzy inference system (FIS) [101]. The steps involved in an inference system are illustrated in Figure 1.  
1. The process of fuzzification converts the effort controllers' feature values into appropriately linguistic fuzzy information. Grouping variables is a difficult operation to complete. Numerous techniques have been established in the literature, including fuzzy clustering [102], genetic algorithms [103], inductive learning [104], neural network-based methods [105], and statistical applications [106]. This paper used statistical applications to partition data spaces into fuzzy sets. 2. The knowledge and rules needed to derive the outputs are stored in a fuzzy rule base. The if-then structure is used to express these rules. Building fuzzy if-then rules  
Figure 1: A structure of FIS. can be achieved using the information that is provided and/or expert knowledge. A transition between the input and output fuzzy sets is provided by the rules. While the rules are connected with a logical \"or\" conjunction, presumed part input fuzzy membership functions (MFs) are combined alternately with the logical \"and\" or \"or\" conjunction. 3. A fuzzy inference engine uses relevant fuzzy rules and existing facts to derive a reasonable conclusion while accounting for human emotions, cognitive processes, and logical reasoning. The creation of the consequent MFs based on the membership degrees of the premise (previous) part and finally is known as the implication part of a fuzzy system. A triangle membership function, which can be written as follows, was employed in this research: \[\mu_{\Delta}(x)=\left\{ \begin{array}{rl}0,&x\leq x_{1}\\ \frac{x-x_{1}}{x_{2}-x_{1}},&x_{1}\leq x\leq x_{2}\\ \frac{x_{3}-x_{2}}{x_{3}-x_{2}},&x_{2}\leq x\leq x_{3}\\ 0,&x>x_{3}\end{array} \right.\] in which \(x_{1}\), \(x_{2}\), and \(x_{3}\) are arbitrary-valued numerical parameters arranged according to the order of magnitude \(x_{1}<x_{2}<x_{3}\). A triangle membership function is correlated with each of the six input variables. Inference operators can be broadly classified into two categories: minimization (min) and product (prod). 4. The process of defuzzification is what converts the fuzzy outputs of the fuzzy system into values that are clear. Numerous defuzzification techniques exist, including weighted sum (Wtsum) and weighted average (Wtaver). There are primarily two methods used in the control and forecasting applications of fuzzy systems: the Mamdani and TS methods [107]. The primary distinction between the Mamdani and TS approaches stems from the process of defuzzification.