Fuzzy logic is a form of many-valued logic that allows intermediate truth values between true and false, such as mostly true. It is used to handle concepts of partial truth and uncertainty. Fuzzy logic algorithms consider all available data to make decisions and mimic how humans reason with possibilities between strict digital values. Fuzzy logic systems use membership functions to quantify linguistic terms, rules and inferences to determine system outputs, and defuzzification to convert fuzzy outputs to non-fuzzy values. Common applications of fuzzy logic include automatic control systems, consumer electronics, and automotive systems.

2. What Is Fuzzy Logic?
Fuzzy Logic is defined as a many-valued logic form which may have truth
values of variables in any real number between 0 and 1. It is the handle
concept of partial truth. In real life, we may come across a situation where we
can’t decide whether the statement is true or false. At that time, fuzzy logic
offers very valuable flexibility for reasoning.
Fuzzy logic algorithm helps to solve a problem after considering all
available data. Then it takes the best possible decision for the given the input.
The FL method imitates the way of decision making in a human which
consider all the possibilities between digital values T and F.

3. Characteristics of Fuzzy Logic
Here, are some important characteristics of fuzzy logic:
Flexible and easy to implement machine learning technique
Helps you to mimic the logic of human thought
Logic may have two values which represent two possible solutions
Highly suitable method for uncertain or approximate reasoning
Fuzzy logic views inference as a process of propagating elastic constraints
Fuzzy logic allows you to build nonlinear functions of arbitrary complexity.
Fuzzy logic should be built with the complete guidance of experts

4. Fuzzy Logic Architecture

5. Fuzzy Logic vs. Probability

6. Crisp vs. Fuzzy

7. Fuzzy Logic Examples

8. Fuzzy Logic Systems Architecture

9. Membership Function: Membership functions allow you to quantify linguistic
term and represent a fuzzy set graphically. A membership function for a
fuzzy set A on the universe of discourse X is defined as μA:X → [0,1].

10. Example of a Fuzzy Logic System:
Let us consider an air conditioning system with 5-level fuzzy logic system. This
system adjusts the temperature of air conditioner by comparing the room
temperature and the target temperature value.

11. Algorithm:
Define linguistic Variables and terms (start)
Construct membership functions for them. (start)
Construct knowledge base of rules (start)
Convert crisp data into fuzzy data sets using membership functions.
(fuzzification)
Evaluate rules in the rule base. (Inference Engine)
Combine results from each rule. (Inference Engine)
Convert output data into non-fuzzy values. (defuzzification)

12. Application Areas of Fuzzy Logic:
The key application areas of fuzzy logic are as given −
Automotive Systems
Automatic Gearboxes
Four-Wheel Steering
Vehicle environment control
Consumer Electronic Goods
Hi-Fi Systems
Photocopiers
Still and Video Cameras
Television

14. Fuzzy Inference Systems

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