1. Fuzzy Logic in AI – Objective
In this Fuzzy Logic Tutorial, we will learn What is Fuzzy Logic Systems in Artificial Intelligence. Moreover, we will discuss the Application and Architecture of Fuzzy Logic in AI. Along with this, we will learn why Fuzzy logic is used and what are its pros and cons.
So, let’s start our journey of Fuzzy Logic System in AI.
Fuzzy Logic Systems in Artificial Intelligence
2. Introduction to Fuzzy Logic in AI
a. What is 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.
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
, it can be implemented
in systems with 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
3. Why Fuzzy Logic?
Generally, we use fuzzy logic system for the practical as well as commercial purposes.
- We can use it to consumer products and control machines.
- Although, not give accurate reasoning, but acceptable reasoning.
- Also, this logic helps to deal with the uncertainty in engineering.
5. Fuzzy Logic Systems Architecture
Basically, four parts are shown in the architecture of fuzzy logic system-
We use this module to transform the system inputs. As tha is is crisp number. Also, helps in splitting the input signal into various five steps.
- LP – x is Large Positive.
- MP- x is Medium Positive.
- MN – x is Medium Negative.
In this, we have to store it in IF-THEN rules that was provided by experts.
Generally, it helps in simulating the human reasoning process. That is by making fuzzy inference on the inputs and IF-THEN rules.
d. Defuzzification Module
In this module, we have to transform fuzzy set into a crisp value. That set was obtained by an inference engine.
Although, the membership functions always work on a same concept i.e fuzzy sets of variables.
AI Fuzzy Logic – Defuzzification Module
6. Membership Function
As this function allows you to quantify linguistic term. 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 −
Fuzzy Logic System – Membership Function
There is some common triangular membership function as compared to other functions. Such as singleton, Gaussian. And trapezoidal.
7. Fuzzy Logic Applications
There are some areas of fuzzy logic system. These are-
a. Automotive Systems
- Vehicle environment control
b. Consumer Electronic Goods
c. Domestic Goods
d. Environment Control
- Air Conditioners/Dryers/Heaters
8. Advantages of Fuzzy Logic Systems
- Generally, in this system, we can take imprecise, distorted, noisy input information.
- Also, these logics are easy to construct and understand.
- Basically, it’s 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 FLS system.
9. Disadvantages of Fuzzy Logic Systems
- Till no designing approach to this fuzzy logic.
- Basically, if logics are simple, then one can understand it.
- Also, suitable for problems which do not have high accuracy.
So, this was all about Fuzzy Logic systems in AI. Hope you like our explanation.
As a result, we have studied Fuzzy Logic systems in AI. Also, implementation, need etc. As this will help you to understand in a better manner with the help of images. Furthermore, if you feel any query, feel free to ask in the comment section.