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:
Good overview articles:
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 T membership functions rule base max, average, centroid, singleton, etc