Nearly 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 linquistic
variable. These are values which don't necessarily directly relate to 
cold hard numbers, but, they do in a roundabout way. 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 is called fuzzy logic: each
fuzzy set carries some tolerance for imprecision. The tolerance for ambiguity helps us model 
the world in a more realistic form by using language rather than cold hard numbers.
With words we can quickly convey ideas like "young" and "old" and quickly make actions
based on this knowledge. Since there is no definitive answer on what is the
cut of for being old/young, we can use fuzzy logic to deal with partial truth values.

# 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

# Fuzzy Logic System



# Example
