# WHAT IS FUZZY LOGIC SYSTEMS IN AI

Author: Rinu Gour

# a. What is a Fuzzy Logic System?

Generally, it’s a method of reasoning. Although, resembles human reasoning. Also, it has an approach to decision-making in humans. As they involve all intermediate possibilities between digital values YES and NO.

The fuzzy Logic System was invented by Lotfi Zadeh. Also, he observed, unlike other computers, it includes a range of possibilities between YES and NO, in a human decision.

# b. Implementation of Fuzzy Logic System

Basically, it can be implemented in systems of various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in artificial intelligence.

# Why Fuzzy Logic?

Generally, we use the fuzzy logic system for practical as well as commercial purposes.

- We can use it for consumer products and control machines.
- Although, not give accurate reasoning, but acceptable reasoning.
- Also, this logic helps to deal with the uncertainty in engineering.

Read more about What is Expert System in Artificial Intelligence

# Fuzzy Logic Systems Architecture

Basically, four parts are shown in the architecture of fuzzy logic system-

a. Fuzzification Module

We use this module to transform the system inputs. As the is a crisp number. Also, helps in splitting the input signal into various five steps.

- LP — x is Large Positive.
- MP- x is Medium Positive.
- S — x is Small.
- MN — x is Medium Negative.
- LN — x is Large Negative

b. Knowledge Base

In this, we have to store it in IF-THEN rules that were provided by experts.

c. Inference Engine

Generally, it helps in simulating the human reasoning process. That is by making fuzzy inferences on the inputs and IF-THEN rules.

d. Defuzzification Module

In this module, we have to transform the fuzzy set into a crisp value. That set was obtained by an inference engine.

Although, the membership functions always work on the same concept i.e fuzzy sets of variables.

# Membership Function

This function allows you to quantify linguistic terms. Also, represent a fuzzy set graphically. Although, MF for a fuzzy set A on the universe of discourse. That X is defined as μA:X → [0,1].

In this function, between a value of 0 and 1, each element of X is mapped. We can define it as the degree of membership. Also, it quantifies the degree of membership of the element. That is in X to the fuzzy set A.

- x-axis– It represents the universe of discourse.
- y-axis — It represents the degrees of membership in the [0, 1] interval.

We can apply different membership functions to fuzzify a numerical value. Also, we use simple functions as complex. As they do not add more precision in the output.

We can define all membership functions for LP, MP, S, MN, and LN. That is shown as below −

There is some common triangular membership function as compared to other functions. Such as singleton, Gaussian. And trapezoidal.

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# Fuzzy Logic Applications

There are some areas of a fuzzy logic system. These are:

# a. Automotive Systems

- Automatic Gearboxes
- Four-Wheel Steering
- Vehicle environment control

# b. Consumer Electronic Goods

- Hi-Fi Systems
- Photocopiers
- Still and Video Cameras
- Television

# c. Domestic Goods

- Microwave Ovens
- Refrigerators
- Toasters
- Vacuum Cleaners
- Washing Machines

# d. Environment Control

- Air Conditioners/Dryers/Heaters
- Humidifiers

# Advantages of Fuzzy Logic Systems

- Generally, in this system, we can take imprecise, distorted, noisy input information.
- Also, this logic is easy to construct and understand.
- Basically, it’s a solution to complex problems. Such as medicine.
- Also, we can relate math in concept within fuzzy logic. Also, these concepts are very simple.
- Due to the flexibility of fuzzy logic, we can add and delete rules in the FLS system.

# Disadvantages of Fuzzy Logic Systems

- Till no designing approach to this fuzzy logic.
- Basically, if logic is simple, then one can understand it.
- Also, suitable for problems that do not have high accuracy.

So, this was all about Fuzzy Logic systems in AI. Hope you like our explanation.

# Conclusion

As a result, we have studied Fuzzy Logic systems in AI. Also, implementation, need, etc. This will help you to understand in a better manner with the help of images. Furthermore, if you feel any queries, feel free to ask in the comment section.

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Source : https://medium.com/@rinu.gour123/what-is-fuzzy-logic-systems-in-ai-cd7254328fc

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