In day to day activities, we may face the things which are not precise. In other terms, we can say the things which are uncertain and unclear. These uncertain values represent fuzzy logic. In this blog, we will discuss Fuzzy logic in Artificial Intelligence.

What is Fuzzy logic?

The method of giving reasoning the same as human beings do. How human beings are making decisions, same in case of fuzzy. It takes the values and all possible outcomes between 0 and 1.

In a boolean system truth value, 1.0 represents complete truth value and 0.0 represents the complete false value. In fuzzy logic, there is in-between value too present which is either partially true or partially false term fuzzy logic was first used in 1965 by Lotfi Zadehwho was a professor of UC Berkeley in California. He observed that orthodox computer logic was not capable of operating data demonstrating particular or unclear human ideas.

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Features of Fuzzy logic

  1. It should be built with proper expert’s guidance.
  2. It is very flexible and easy for implementation.
  3. It helps in building non-linear functions even having complexity with it.
  4. It is a good method for finding out uncertain results.
  5. It helps in human thoughts mimicry.

 Advantages of Fuzzy logic

  1. It gives an efficient solution for complex problems.
  2. It helps in dealing with uncertainty.
  3. It doesn’t need precise inputs.
  4. It can help in altering inputs whenever required.
  5. It is used for commercial applications.
  6. It offers acceptable reasoning.

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Disadvantages of Fuzzy logic

  1. It needs to be verified.
  2. It sometimes creates confusion.
  3. Verification and validation need extreme testing.
  4. Setting the membership function is difficult.
  5. Fuzzy systems don’t have the proficiency of machine learning as-well-as neural network type pattern recognition.

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Boolean and Fuzzy representation

Fuzzy logic in Artificial Intelligence

The architecture of Fuzzy logic

The architecture contains the following parts:

  • Rule base
  • Fuzzification
  • Inference engine
  • Defuzzification
  • Membership function

Rule base

The rule base contains all rules. In rule-based if-then conditions are used. These contents are provided by experts. There is a recent modification in the fuzzy theory that gives various methods for the design as well as tuning of fuzzy controllers. The latest developments in fuzzy logic help in reducing the number of fuzzy rules.

Fuzzification

Its main role of fuzzification is:

  • It converts inputs or the crisp numbers into fuzzy sets.
  • You can measure the crisp inputs by sensors and pass them into the control system for further processing

Inference engine

It exactly matches out the degree of fuzzy input and the rules. By combining the fired rules, control action is formed.

Defuzzification

It is just the opposite of fuzzification. The Defuzzification process transforms the fuzzy sets into a crisp value. There is a number of techniques available, and you need to choose the best-suited one with an expert system.

Membership function

The membership function is a graph that defines how each point in the input space is mapped to membership value between 0 and 1.

Applications of fuzzy logic

  1. Fuzzy logic mimics the human decision-making process, it makes decisions very accurate and faster.
  2. It is deployed in Natural language processing
  3. Fuzzy logic is used in chemical distillation in a chemical factory
  4. It helps in controlling speed and traffic
  5. Fuzzy logic is used in aircraft for height control of spacecraft.

Conclusion

In this blog, we have read about Fuzzy logic in Artificial Intelligence. If you feel any doubt, ask freely in the comment section.

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