There are two major types of fuzzy system, Mamdani and Sugeno [32]. Mamdani FIS is the most commonly used fuzzy rule-based model because of its inherent characteristic of handling nonlinear relationship between inputs and output.

Fuzzy rule-based models are a form of fuzzy model which is particularly well-suited to human centric tasks due to the high degree of readability and interpretability conveyed to an expert reader.

Fuzzy models or sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and utilising data and information that are vague and lack certainty.

Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process of natural evolution, to identify its structure and parameter.

Take the linguistic variable of the fuzzy input and the corresponding linguistic variable of the fuzzy output….The steps of rule extraction are defined briefly as follows:

1. Choose the fuzzy inputs X and outputs Y.
2. Define their universal set and fuzzy set.
3. Define the linguistic variables and their membership functions.

FUZZY INFERENCE SYSTEM Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made or patterns discerned.

Fuzzy logic is an approach to variable processing that allows for multiple possible truth values to be processed through the same variable. Fuzzy logic attempts to solve problems with an open, imprecise spectrum of data and heuristics that makes it possible to obtain an array of accurate conclusions.

A rule-based expert system has five components: the knowledge base, the database, the inference engine, the explanation facilities, and the user interface.

Fuzzy set theory permits membership function valued in the interval [0,1]. Example: Words like young, tall, good or high are fuzzy. Fuzzy set theory is an extension of classical set theory where elements have degree of membership.

The probability theory is based on perception and has only two outcomes (true or false). Fuzzy theory is based on linguistic information and is extended to handle the concept of partial truth. Fuzzy values are determined between true or false.

Many control that used to operate the converter, one of these is Fuzzy Logic Controller (FLC). FLC is used because it has advantages such as fast response and good performance. FLC is a rule-based control system, so the basic rules are made to operate this system by input processing.

In crisp logic, the premise x is A can only be true or false. However, in a fuzzy rule, the premise x is A and the consequent y is B can be true to a degree, instead of entirely true or entirely false. This is achieved by representing the linguistic variables A and B using fuzzy sets.