Fuzzy System has been existed since 1920, that proposed by Lukasiewicz. Fuzzy System existed to handle gray interpretation, which means to handle variable that have non-crisp values or bold values (crisp values : True/False). Fuzzy System likely more handling the variable that have non-crisp value or we called degrees of membership, for example the variable that has values between 0..1
"FUZZY LOGIC IS A BRANCH OF THE MEMBERSHIP DEGREES COMPARED USING EXPRESS MEMBERSHIP (TRUE / FALSE)"Fuzzy Logic
Fuzzy Logic more focused on quantitying and reasoning of fuzzy that comes from natural language. Value that comes from natural language is more known as Linguistic Variable / Fuzzy Variable . For example above
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| Fuzzy |
Fuzzy Rule
On Fuzzy System, Linguistic Variable is used on Fuzzy Rule. Example of Fuzzy Rule :
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| Fuzzy Rule |
In crisp logic, we just have 2 values, True and False, but in Fuzzy, we have membership degrees, or range 0 to 1 in values.
To make Fuzzy Sets, we need the membership function. To define membership function, there are many methods, for example : polling, define itself, questioner, etc. Example of fuzzy : Short (4..6) Medium (4,5..6,5) Tall (5..7) and the fuzzy sets are like this :
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| Fuzzy Sets |
- Linier Function
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| Linier |
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| Sigmoid |
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| Triangle |
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| Trapesium |
- Bell Function
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| Bell 1 |
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| Bell 2 |
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| Bell 3 |
Linguistic variable is a numerical interval and have linguistic values, the semantics is defined by its membership function. For example, temperature is a linguistic variable that can be defined on the interval [-100C, 400C]. These variables could have linguistic values such as 'Cold', 'Warm', 'Heat' which semantic functions are defined by certain membership.
That's all about Fuzzy, next we will learn about Mamdani in Fuzzy.
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