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2 ( a ) Given the linguistic input variables, Age and Height, each with the given terms: Age young = 1 0 + 0 .

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(a) Given the linguistic input variables, Age and Height, each with the given terms:
Age
young =10+0.910+0.820+0.530+0.240+0.050
middle =0.220+0.530+0.740+150+0.750+0.560+0.270
old =0.140+0.350+0.560+0.770+0.980+190+1100
Height
short =11.4+0.751.5+0.51.6+0.251.7
average =0.51.6+10.7+0.51.8
tall =0.21.6+0.51.7+0.751.8+11.9+12.0
and the single linguistic output variable Employ, with the three terms:
Employ
bad =10+11+0.72+0.53+0.34
fair =0.13+0.54+15+0.56+0.17
good =0.36+0.57+0.78+19+110
calculate the fuzzy set result of applying Mamdani inference on input values of Age=40 and Height =1.6 when using the rules:
R1: IF Age is young OR Height is tall THEN Employ is good
R2: IF Age is middle THEN Employ is fair
R3: IF Age is old AND Height is short THEN Employ is bad
Note that you should not defuzzify the output set at this stage.
(b) Describe and give basic formulae for two alternative methods of numeric defuzzification.
Provide an example of two obviously different output sets for which one of these defuzzification methods gives the same result.
(c) Calculate the defuzzified value of your output set from Q2(a) above, using centre-of-gravity defuzzification. If you have not been able to derive a set for Q2(a), then write down your own fuzzy set defined on a universe of discourse of the integers from 0 to 10, and calculate its defuzzified value.
(d) Describe the role of similarity measures in the process of linguistic defuzzification. Give examples of two different similarity measures.
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