Initial draft of fuzzy logic blog post.

blogContent/posts/data-science/fuzzy-logic.md

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+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