The Net's Original Fuzzy Logic Archive - Since 1994

Fuzzy Logic Basics

Allen Bonde

Fuzzy Logic is a departure from classical two-valued sets and logic, that uses “soft” linguistic (e.g. large, hot, tall) system variables and a continuous range of truth values in the interval [0,1], rather than strict binary (True or False) decisions and assignments. Formally, fuzzy logic is a structured, model-free estimator that approximates a function through linguistic input/output associations. Fuzzy rule-based systems apply these methods to solve many types of “real-world” problems, especially where a system is difficult to model, is controlled by a human operator or expert, or where ambiguity or vagueness is common. A typical fuzzy system consists of a rule base, membership functions, and an inference procedure (see Figure). Some Fuzzy Logic APPLICATIONS include: The key BENEFITS of fuzzy design are:

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Good overview articles: Classic Papers: Excellent Books: Journals that cover fuzzy logic:
   ________________     _______________     ________________
I  |               |    |              |    |               | O
N  |Crisp-to-Fuzzy |    |  Inference   |    |Fuzzy-to-Crisp | U
P  |               |--->|              |--->|               | T
U  |    FUZZIFY    |    | max-min, etc |    |   DEFUZZIFY   | P
T  |_______________|    |______________|    |_______________| U
  membership functions     rule base          max, average,
                                              singleton, etc