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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
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- 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
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- # Fuzzy Rules
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- # Fuzzy Logic System
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- # Example
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