In the fuzzification subprocess, the


In the fuzzification subprocess, the membership functions defined on the

input variables are applied to their actual values, to determine the

degree of truth for each rule premise. The degree of truth for a rule's

premise is sometimes referred to as its ALPHA. If a rule's premise has a

nonzero degree of truth (if the rule applies at all...) then the rule is

said to FIRE. For example,

, , , . . ( ...) ( ). ,

x y low(x) high(x) low(y) high(y) alpha1 alpha2 alpha3 alpha4

------------------------------------------------------------------------------

0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0

0.0 3.2 1.0 0.0 0.68 0.32 0.68 0.32 0.0 0.0

0.0 6.1 1.0 0.0 0.39 0.61 0.39 0.61 0.0 0.0

0.0 10.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0

3.2 0.0 0.68 0.32 1.0 0.0 0.68 0.0 0.32 0.0

6.1 0.0 0.39 0.61 1.0 0.0 0.39 0.0 0.61 0.0

10.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0

3.2 3.1 0.68 0.32 0.69 0.31 0.68 0.31 0.32 0.31

3.2 3.3 0.68 0.32 0.67 0.33 0.67 0.33 0.32 0.32

10.0 10.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0

In the inference subprocess, the truth value for the premise of each rule is

computed, and applied to the conclusion part of each rule. This results in

one fuzzy subset to be assigned to each output variable for each rule.


    





Forekc.ru
, , , , , , , , , ,