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      • Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors.

      Fuzzy Logic for Intelligent Control System Using Soft ...

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  3. www.geeksforgeeks.org › difference-between-crispDifference Between Crisp Set and Fuzzy Set - GeeksforGeeks

    May 30, 2021 · Hence, the measurement of the membership of the elements from the universe in the set against a function for detecting the uncertainty and ambiguity. Crisp set defines the value is either 0 or 1. Fuzzy set defines the value between 0 and 1 including both 0 and 1. It is also called a classical set.

  4. en.wikipedia.org › wiki › Fuzzy_logicFuzzy logic - Wikipedia

    Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. [1] .

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  6. en.wikipedia.org › wiki › Fuzzy_setFuzzy set - Wikipedia

    Definition. A fuzzy set is a pair where is a set (often required to be non-empty) and a membership function. The reference set (sometimes denoted by or ) is called universe of discourse, and for each the value is called the grade of membership of in . The function is called the membership function of the fuzzy set .

  7. medium.com › @harshhvm › exploring-soft-computingExploring Soft Computing: Fuzzy Logic, Neural ... - Medium

    Fuzzy logic provides a framework for dealing with ambiguity and imprecision in decision-making. Traditional binary logic operates in a world of absolutes — true or false, 0 or 1. In contrast,...

    • Harshvardhan Mishra

  8. link.springer.com › chapter › 10Introduction to Fuzzy Sets | SpringerLink

    Oct 18, 2017 · 11 Citations. Abstract. The subject of this chapter is fuzzy sets and the basic issues related to them. The first section discusses concepts of sets: classic and fuzzy, and presents various ways of describing fuzzy sets. The second section is dedicated to t -norms, s -norms, and other terms associated with fuzzy sets.

  9. www.cs.rpi.edu › courses › fall03Fuzzy Rules and Fuzzy Reasoning

    1. m ( x ) / x +. 2 2. +m ( x ) / n. x. A n. The image of A under f( ) is a fuzzy set B:, i.e., B=f(A) = m ( x ) / + m ( x ) / y + 2. 1 B 2. +m ( x. ) / y n. B. where yi = f(xi), i = 1 to n. If f( ) is a many-to-one mapping, then. 4 m ( y ) =

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