Fuzzy Logic is a methodology predicated on the idea that the “truthiness” of something can be expressed over a continuum. This is to say that something isn’t true or false but instead partially true or partially false.
A fuzzy variable has a crisp value which takes on some number over a pre-defined domain (in fuzzy logic terms, called a universe). The crisp value is how we think of the variable using normal mathematics. For example, if my fuzzy variable was how much to tip someone, it’s universe would be 0 to 25% and it might take on a crisp value of 15%.
A fuzzy variable also has several terms that are used to describe the variable. The terms taken together are the fuzzy set which can be used to describe the “fuzzy value” of a fuzzy variable. These terms are usually adjectives like “poor,” “mediocre,” and “good.” Each term has a membership function that defines how a crisp value maps to the term on a scale of 0 to 1. In essence, it describes “how good” something is.
So, back to the tip example, a “good tip” might have a membership function which has non-zero values between 15% and 25%, with 25% being a “completely good tip” (ie, it’s membership is 1.0) and 15% being a “barely good tip” (ie, its membership is 0.1).
A fuzzy control system links fuzzy variables using a set of rules. These rules are simply mappings that describe how one or more fuzzy variables relates to another. These are expressed in terms of an IF-THEN statement; the IF part is called the antecedent and the THEN part is the consequent. In the tiping example, one rule might be “IF the service was good THEN the tip will be good.” The exact math related to how a rule is used to calcualte the value of the consequent based on the value of the antecedent is outside the scope of this primer.
Taking the tipping example full circle, if we were to create a controller which estimates the tip we should give at a restaurant, we might structure it as such: