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All applications of Fuzzy logic rely on the notion of linguistic variables. These are variables whose values are words rather than cold hard numbers. Something like "it is nice outside" is an examples of a linguistic variable. These are values which map to conceptual property rather than numerical numbers. When I say that it is nice outside, that is subjective to my opinion; other people may have different opinions on what is considered nice outside. That is why this field is called fuzzy logic: each fuzzy set carries some tolerance for imprecision. This tolerance for ambiguity helps us model the world in a more versatile way because it allows us to language for computation.
With words we can quickly convey ideas like "hot" and "cold" and take actions. Since there is no definitive answer on what is the cut of for being hot/cold, we can use fuzzy logic to model the ambiguity and deal with partial truth values. For example, it is possible to be 60% cold and 20% hot in a fuzzy logic system. If it is hot, we want to turn up the fans, if it is cold we want to turn off the fan. Knowing the partial truth values we may decide to turn the fans on at 10%.
The remainder of this blog post will dive into the details of each component of a fuzzy logic system.
Fuzzy Sets
Classical sets are mutually exclusive. In other words: things can only belong to one set at a time. In a fuzzy set, elements can belong to multiple sets with some degree of membership. As an example, someone who is 30 may be 33% in the young set and 66% in the old set. Fuzzy sets are usually are represented by trapezoids; however, other shapes such as gaussian can be used.
Temperature Example
Fuzzy Rules
De-fuzzification
Fuzzy Logic System
-------lucid chart diagram of fuzzifier, rule, deffizifier
Example
--javascript code?