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  2. Ebrahim Mamdani - Wikipedia

    en.wikipedia.org/wiki/Ebrahim_Mamdani

    Abe Mamdani was an Emeritus Professor at Imperial College London. He received the "European Fuzzy Pioneer Award" from the European Society for Fuzzy Logic and Technology (EUSFLAT) in 1999, and the "Fuzzy Systems Pioneer Award" from the IEEE Computational Intelligence Society in 2003.

  3. Fuzzy control system - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_control_system

    A fuzzy control system is a control system based on fuzzy logic – a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively). [1] [2]

  4. Genetic fuzzy systems - Wikipedia

    en.wikipedia.org/wiki/Genetic_fuzzy_systems

    1974, E.H. Mamdani, Applications of fuzzy algorithms for control of simple dynamic plant, Proc. IEE 121 1584 - 1588. 1995, A. Bastian, I. Hayashi: "An Anticipating Hybrid Genetic Algorithm for Fuzzy Modeling", Journal of Japan Society for Fuzzy Theory and Systems, Vol.10, pp. 801–810

  5. Fuzzy logic - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_logic

    Fuzzy logic is an important concept in medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy-logic-based approaches. Fuzzy logic can be used in many different aspects within the medical decision making framework.

  6. Neuro-fuzzy - Wikipedia

    en.wikipedia.org/wiki/Neuro-fuzzy

    Neuro-fuzzy hybridization is widely termed as fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules.

  7. Type-2 fuzzy sets and systems - Wikipedia

    en.wikipedia.org/wiki/Type-2_fuzzy_sets_and_systems

    Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of much uncertainty.

  8. This disease kills more people than all cancers and accidents ...

    www.aol.com/disease-kills-more-people-cancers...

    In 2022 — the most recent year for which mortality data is available — a total of 941,652 people died of heart disease-related condition

  9. Defuzzification - Wikipedia

    en.wikipedia.org/wiki/Defuzzification

    The place of defuzzification in a fuzzy control system A particular defuzzification method. Defuzzification is the process of producing a quantifiable result in crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.