In real life, we may encounter situations that are not easy to deal with, and there’s no definite answer to them. We have written this post with the help of Artificial Intelligence experts; let’s have a look at how Fuzzy Logic describes such situations in Artificial Intelligence.
Learning Of Blog
- Introduction
- What is Fuzzy Logic?
- How Fuzzy Logic Works
- Advantages of Fuzzy Logic Systems
- Real-Life Applications of Fuzzy Logic
- Conclusion
Introduction
Can you answer every question with a Yes or No? No, that’s because there are a lot of real-life situations where there is no adequate data available to come up with a definite answer to a question. Or, we may not be sure ourselves. For example, if your wife asks you to plan a vacation for next month, you cannot be entirely confident and cannot say Yes or No. It’s because you are not sure about the circumstances that may arise in a month.
According to Artificial Intelligence experts, Fuzzy Logic is a representation of such scenarios where the input data is unclear.
What is Fuzzy Logic?
Computers mainly use Boolean Logic to determine the result of scenarios. As per the Boolean Logic, value 1 refers to True, and 0 means False. The term Fuzzy refers to something unclear or vague. The computer cannot easily understand such cases. Thus, it cannot produce an exact result of True or False. But a Fuzzy Logic algorithm makes systems more intelligent and helps them understand the problems where there may be other answers than true or false.
Lotfi Zadeh was the first to describe the term Fuzzy Logic in 1965. He thought that as humans cannot answer every question with a Yes or No, traditional computers are also not capable of processing unclear data. These uncertainties can be:
- Definitely yes
- Possibly yes
- Can’t say
- Possibly no
- Definitely no
How Fuzzy Logic Works
Let’s try to understand the Fuzzy Logic with the help of the following example.
Problem – Is it cold outside?
Boolean Logic
- Yes
- No
Fuzzy Logic
- Very cold
- Little cold
- Moderately cold
- Not at all
As you can see, Fuzzy Logic addresses the problem better, such as very cold, moderately cold, or not cold. These answers are displayed with the help of values between 0 and 1.
So, in cases where an accurate answer cannot be provided, Fuzzy Logic provides satisfactory reasoning. A Fuzzy Logic, coupled with a good algorithm takes into account all the available data and then comes up with the best possible solution.
Let’s take a look at another example:
Problem – Is the fuel tank full?
Boolean Logic
- Yes
- No
Fuzzy Logic
- Full
- Almost full
- Half Full
- Almost empty
- Empty
In the example above, we can see that Fuzzy Logic is a better way to answer the question as a fuel tank is mostly neither full nor empty.
Advantages of Fuzzy Logic Systems
Some benefits of Fuzzy Logic systems are:
- A robust system that works with vague inputs
- Acceptable input types are imprecise, distorted or uncertain data
- The feedback sensor can be reprogrammed if it stops working
- The Fuzzy Logic algorithms do not occupy a huge memory space
- Fuzzy Logic systems are used to solve complex problems
- Systems with a simple structure
Real-Life Applications of Fuzzy Logic
The Fuzzy Logic can be used in a variety of industries, including domestic goods, automotive systems, environment control, etc. Some of them are:
- It is used to control the altitude of aircraft, satellites, and spaceships.
- It is used in automotive systems to monitor and control the traffic and speed.
- Large companies used it for personal evaluation and decision making support systems.
- The chemical industry uses Fuzzy Logic for processes like controlling the pH.
- Fuzzy Logic, coupled with technologies like Natural language processing and Artificial Intelligence, can enhance the capabilities of systems.
- It is extensively used in systems to automate vehicle control.
- The purpose of using Fuzzy Logic is to make decisions like a human in case of unclear data, but faster. Thus, making it suitable for Neural Networks.
Conclusion
Fuzzy Logic functions like the human brain, making it a necessity in expert systems, artificial intelligence, and neural networks. When vague data is input, the AI-based Fuzzy Logic system might be your best friend in finding the solution.
Also, if you wish to understand Fuzzy Logic and AI in-depth, it’s a good idea to check out some AI certificate programs such as the Certified Artificial Intelligence Expert or Certified Artificial Intelligence Developer.