When you have to make a decision, how many times it’s a simple Yes or No? Many times, your choices are a variation of Yes or No questions, where you have multiple degrees of options present between the two.
Suppose someone asks you, “Will you go to that party tonight?” Your answer would probably be ‘Maybe yes’ or ‘Certainly not’, instead of a straightforward Yes or No.
Human decision making relies a lot on such vague choices. And in AI, this concept is called Fuzzy logic.
In this fuzzy logic tutorial, you’ll get to learn what fuzzy logic is, how it works, and what are its applications. Let’s get started.
What is Fuzzy Logic? Explanation
Fuzzy refers to a lack of clarity. There are many instances when you can’t make a simple ‘Yes or No’ decision. In those cases, you’ll need to make a dynamic choice. Fuzzy logic helps you in that regard.
It gives you a flexible choice and enables you to reason with more data and variables. There are many applications of fuzzy logic in AI because you can’t use Boolean or similar logic everywhere. It resembles human reasoning and considers all the possibilities that exist between Yes and No of choice.
Usual logic only considers two possibilities: Yes and No. But humans don’t make decisions like that. They have many possibilities between Yes and No such as, Possibly Yes, and Possibly No. Fuzzy logic takes into account all of those possibilities.
A Little History
Lotfi Zadeh, a computer scientist, and a mathematician came up with fuzzy logic when he realized that there is a big difference between the decision making of humans and computers. He pointed out that conventional computer logic couldn’t manipulate data to represent unclear ideas similar to humans.
As we mentioned earlier, there are many applications of fuzzy logic in AI. But you can see it in many other fields too as it enables artificial intelligence to distinct data that doesn’t align with Boolean logic. This means that data is neither true nor false.
Qualities of Fuzzy Logic
Fuzzy logic might seem a little complicated, but it’s a flexible machine learning technique that you can implement very easily. It allows you to imitate human thought. It’s also perfect for solving issues where uncertainty is high.
It enables you to construct nonlinear functions that possess arbitrary complexity. But you should ensure that you build it after having sufficient knowledge of the same because it’s straightforward to make mistakes in this case.
When Should You Not Use Fuzzy Logic
There are a few cases when you shouldn’t use fuzzy logic. Here are some of them:
- When you can use conventional logic to arrive at a solution, you don’t need to use fuzzy logic because it would overcomplicate everything
- Controllers don’t necessarily require fuzzy logic
- If you can’t map the input to the output, you shouldn’t use fuzzy logic
How does Fuzzy Logic Work? (Architecture)
The architecture of fuzzy logic has the following components:
- Inference (Intelligence)
Rules: All the rules of the fuzzifier remain stored in the Rule Base. All the IF-THEN conditions you enter in the logic also remain here so that it can make the decisions. Recent updates in fuzzy logic enable AI experts to reduce the number of rules they need.
Fuzzifier: Fuzzifier converts the input (crisp numbers) into fuzzy sets. Its sensors measure the crips inputs and pass them into the control system so the logic can process them further.
Inference: After the Fuzzifier comes the Inference Engine. It finds how much the fuzzy input matches the rules. It also selects which rules it has to apply according to the input field it has.
Defuzzifier: In this stage, the defuzzifier converts the fuzzy sets it receives into crisp output. There are many forms of defuzzification available, so you would need to choose one according to your needs.
Differences Between Fuzzy Logic and Probability
To understand the concept of this fuzzy logic tutorial properly, you’ll have to take a look at the differences between fuzzy logic vs. probability because it confuses many people.
The first major difference between the two is the subject matter they deal with. Fuzzy logic works with facts, whereas probability works with events that may or may not occur. Fuzzy logic conveys partial truth, while probability conveys partial knowledge.
Also, fuzzy logic takes truth as its mathematical basis, whereas probability builds a model of ignorance.
Differences Between Fuzzy and Crisp
There are many differences between the two. First, a crisp time set could be fuzzy, but a fuzzy time set can’t be crisp. The law of Non-contradiction and Excluded Middle might (or might not) hold in Crisp logic. On the other hand, with fuzzy logic, these laws hold.
Crisp logic has strict boundaries; that is, it could be either True or False. And fuzzy logic has multiple degrees of membership.
Also read: Machine Learning Project Ideas for Beginners
What are the Applications of Fuzzy Logic?
The following are the main applications of fuzzy logic in different industries:
Automotive companies use fuzzy logic in AI to create anti-lock brakes. Fuzzy logic can figure out when it’s necessary to apply anti-lock brakes according to the acceleration, and car speed, to avoid an accident.
Another use of fuzzy logic in cars is the auto transmission. In those cases, fuzzy logic controls the ignition and fuel injection according to the RPM and setting of the throttle. Cruise control is a feature where the car maintains a specific speed and allows the driver to take a break from pressing the accelerator.
Fuzzy logic helps in adjusting the throttle setting to maintain the designated car speed.
Omron uses fuzzy logic rules to check the fitness levels of their employees. They have added their specific set of rules for this purpose. With its help, many companies can monitor their employees’ fitness and provide facilities accordingly.
- Microwave ovens use fuzzy logic to choose the right cooking option.
- Golf diagnostic systems apply fuzzy rules to recommend the most suitable golf club according to the golfer’s physique and other factors
- Elevator control systems determine wait time according to the number of passengers they have. They implement fuzzy logic for this purpose.
- Dishwashers use fuzzy logic to choose the right cleaning settings (cycles, rinse and wash time, etc.) according to the dishes they have.
Advantages and Disadvantages of Fuzzy Logic?
- Fuzzy logic has a simple structure that’s very easy to understand
- It has applications in many industries including automobiles and home appliances
- It solves problems of uncertainty with better effectiveness than other algorithms
- Versatile in use because it doesn’t need precise inputs for its operation
- Due to its simple structure, you can modify it easily and enhance its performance
- If feedback sensors don’t work, you can use fuzzy logic to solve those issues
- It is not capable of performing pattern recognition similar to neural networks
- Verifying and validating a system based on fuzzy logic requires a lot of hardware testing
- Setting up fuzzy rules is quite a difficult task
- The output of fuzzy logic isn’t always accurate. This can cause problems in some cases, as the result might not be acceptable.
Now we’ve reached the end of our fuzzy logic tutorial. We hope you found this article useful. Fuzzy logic is surely one of the most popular and simple concepts of artificial intelligence.
If you want to learn more about fuzzy logic and other concepts of AI, then we recommend heading to our blog. You’ll find many helpful articles there.
And if you want a more detailed and structured learning experience, then you should take an AI course and start with that. An artificial intelligence course will give you all the knowledge you need on this topic with much ease.
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What is the Mamdani approach to fuzzy logic?
The Mamdani approach was presented as the first attempt to control a steam engine and boiler system using language control rules gathered from experienced human operators. Each rule in a Mamdani system produces a fuzzy set as its output. Mamdani systems are well-suited to expert systems applications where the rules are developed from human expert knowledge, such as medical diagnostics, since their rule bases are more intuitive and simpler to grasp.
What is the fuzzy approximation theorem (FAT) and its reasoning?
According to Bart Kosko, the fuzzy approximation (FAT) indicates that a fuzzy system may mimic any continuous system. Each rule functions as a fuzzy patch that the system applies to the response in order to make it look like the response of the continuous system. While Fuzzy Approximate Reasoning refers to the method used to derive a probable imprecise conclusion from a set of imprecise premises. In approximate reasoning, fuzzy logic is capable of dealing with many sorts of uncertainty.