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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%.
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- 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
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- # Fuzzy Rules
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- # De-fuzzification
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- # Fuzzy Logic System
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- -------lucid chart diagram of fuzzifier, rule, deffizifier
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- # Example
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- --javascript code?
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