8 Fuzzy Logic and Fuzzy Based Multi Criteria Decision Analysis
53
Fig. 8.2 Membership
functions features (Ozsahin
et al. 2020)
(a) Core:
The core elements of a fuzzy set ˜
A ∈ I R can be determined by the formula below:
μ ˜
A (x) = 1
(23)
(b) Support:
The support elements of the fuzzy set ˜
A ∈ I R can be determined by the formula
below:
μ ˜
A (x) > 0
( 2 4 )
(c) Boundary:
The boundary of the fuzzy set ˜
A ∈ I R can be determined by the formula below:
1 > μ ˜
A (x) > 0
(25)
The general features of a fuzzy set can be seen in Fig. 8.2.
8.3 Fuzzification
Fuzzification is referred to as a process by which crisp input value is converted to
a fuzzy value that works through information from the knowledge base. In other
words, it is seen as a process of decomposing any input or output system into one or
more fuzzy sets. Fuzzification can also be a method described by changing a fuzzy
53
Fig. 8.2 Membership
functions features (Ozsahin
et al. 2020)
(a) Core:
The core elements of a fuzzy set ˜
A ∈ I R can be determined by the formula below:
μ ˜
A (x) = 1
(23)
(b) Support:
The support elements of the fuzzy set ˜
A ∈ I R can be determined by the formula
below:
μ ˜
A (x) > 0
( 2 4 )
(c) Boundary:
The boundary of the fuzzy set ˜
A ∈ I R can be determined by the formula below:
1 > μ ˜
A (x) > 0
(25)
The general features of a fuzzy set can be seen in Fig. 8.2.
8.3 Fuzzification
Fuzzification is referred to as a process by which crisp input value is converted to
a fuzzy value that works through information from the knowledge base. In other
words, it is seen as a process of decomposing any input or output system into one or
more fuzzy sets. Fuzzification can also be a method described by changing a fuzzy