# Fuzzy Thinking

Fuzzy Thinking or Fuzzy logics analyzes data in a range between zero to one as opposed to zero and one logic used by Aristotelian logic. In Fuzzy Logic, there are infinite states between zero and one. Machine logics are Aristotelian that only consider zero and ones, but the human mind thinks in fuzzy terms. Fuzzy logic was introduced to change this aspect of machine operations. Fuzzy logic is a way to extract certain information from fuzzy inputs. In this perspective, its application is similar to Machine Learning. Fuzzy logic has found many applications such as microcontrollers, multi-channel computers, large networks, control systems, software, and hardware. Other areas of Fuzzy Logic applications are Artificial Intelligence and Control Theory. In these fields, computers make calculations and decisions based on uncertain data. The fuzzy process has four main steps mentioned below.

**Basic rules**

This section specifies all the conditions of the system defined by an expert to enable the modification of the decision-making process.

**Fuzzy**

This section defines fuzzy information and inputs. These fuzzy data will become a fuzzy set that can be modified for fuzzy processing.

**Inference or intelligence engine**

In this section, fuzzy rules are used to examine the compliance of the input data from fuzzy. Consequently, decision-making is performed based on the degree of compliance.

**Return from fuzzy**

In this section, fuzzy sets extracted from fuzzy inference are converted into quantitative outputs. Based on outputs, the best decision can be made with different degrees of adaptations.