4 FUZZIFIKASI & DEFUZZIFIKASI FuzzificationScales and maps input variables to fuzzy setsInference MechanismApproximate reasoningDeduces the control actionDefuzzificationConvert fuzzy output values to control signals
7 FUZZIFICATIONTransformation from crisp input to fuzzy input.
8 APPROXIMATE REASONING y = f(x)Then we can make inferences easilypremise y = f(x)fact x = x’consequence y = f( x’ )
9 BASIC INFERENCES x is A Mary is very young A B very young young x is B Mary is youngx is A pressure is not very highx is B pressure is not very lowx is A B pressure is not very high andnot very low
10 MAMDANI’S IMPLICATION OPERATOR if x is A then y is Bx is A’y is B’
11 INFERENCE Penarikan kesimpulan dari semua fungsi keanggotaan yang sudah didefinisikan menggunakan RULE BASE.IF A AND B THEN CRULE 1 : A1 , B1 C1RULE 2 : A2 , B2 C2FAKTA : x , DxKESIMPULAN : C’
13 DEFUZZIFICATION Transformation from fuzzy output to crisp ouput. Defuzzification is a process to get a non-fuzzy value that best represents the possibility distribution of an inferred fuzzy control action.There is no systematic procedure for choosing a good defuzzification strategy.Selection of defuzzification procedure depends on the properties of the application.
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