Fuzzy Control Primer

Tram Ho

  1. Overview of Fuzzy Control Primer

Fuzzy logic is a method built on ambiguity, ambiguity in propositions. Usually when talking about the result of a par so that we usually only have two truth values, either True (TRUE) or False (FALSE). A clause that holds true value will never be false and vice versa.

However, in life, we can not always consider and identify these truth values ​​as in theory. A proposition may be true in one context, but when we put it in another context, it is still true. To solve this problem in 1965, American professor Lotfi Zadeh published a study called Fuzzy Logic. Since then, fuzzy logic has gone through many stages of development: inventions in the US, application in Europe and application to commercial products in Japan.

From the successes, fuzzy logic has become a standard design technique and is widely accepted into the community.

Fuzzy logic often refers to vague things, which are not really obvious. Fuzzy logic is similar to the method of human decision making. It simplifies problems outside the real world and relies on the level of trust in the problem rather than the usual true or false statements like classical logic.

The picture below shows us a fuzzy system, the values ​​will be indicated by the values ​​in the range from 0 to 1. The value 1 here will represent absolute truth and 0 will represent price. absolute falseness. These values ​​will indicate the truth values ​​of the clauses within the fuzzy system.

Fuzzy sets, also called Fuzzy sets, are an extension of classical set theory and are used in fuzzy logic. In the Classical theory set the relationships of the members of the set are evaluated by binary values ​​0 or 1, true or false on a clear proposition – an element will either belong to a set or will not be. belongs to a set.

For example, with the phrase: “Is he tall?” then there will be only two truth values ​​True or False. If he is really tall, the truth value will be 1 and vice versa

Fuzzy theory set, on the other hand, will allow us to evaluate the degree of confidence in the proposition that we evaluate based on a membership function and the value set of this dependent function will be in the paragraph. 0 to 1.

For example, with the phrase: “Is he tall?” then we will have the following truth values:

  • He is very tall (0.8)
  • He is tall (1)
  • He is short (0.2)
  • He is very short (0.0)

Here the numbers 0.8, 1, 0.2, and 0.0 will be truth values ​​that indicate the degree of confidence in a given proposition.

From this we can understand the difference between classical and fuzzy sets simply:

  • The classical set will include elements for which its reliability will be completely accurate.
  • Fuzzy set will consist of molecules whose reliability will depend on membership function.


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Source : Viblo