Computer Engineering Soft Computing MCQ Questions and answers

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Q. The values of the set membership is represented by

(A) discrete set
(B) degree of truth
(C) probabilities
(D) both degree of truth & probabilities

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Q. Given U = {1,2,3,4,5,6,7} A = {(3, 0.7), (5, 1), (6, 0.8)} then A will be: (where ~ → complement)

(A) {(4, 0.7), (2,1), (1,0.8)
(B) {(4, 0.3.): (5, 0), (6
(C) {(l, 1), (2, 1), (3, 0.3)
(D) {(3, 0.3), (6.0.2)}

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Q. If A and B are two fuzzy sets with membership functions
μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8}
μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5}
Then the value of μ(A∪B)’(x) will be

(A) {0.9, 0.5, 0.6, 0.8, 0.8
(B) {0.6, 0.2, 0.1, 0.7,
(C) {0.1, 0.5, 0.4, 0.2, 0.
(D) {0.1, 0.5, 0.4, 0.2, 0.3}

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Q. Compute the value of adding the following two fuzzy integers:
A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)}
Where fuzzy addition is defined as
μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to

(A) {(0.5,12), (0.6,13), (1,
(B) {(0.5,12), (0.6,13),
(C) {(0.3,12), (0.5,13), (
(D) {(0.3,12), (0.5,13), (0.6

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Q. A U (B U C) =

(A) (a ∩ b) ∩ (a ∩ c)
(B) (a ∪ b ) ∪ c
(C) (a ∪ b) ∩ (a ∪ c)
(D) b ∩ a ∪ c

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Q. Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction μA(x) = x / (x+2) Then the α cut corresponding to α = 0.5 will be

(A) {0, 1, 2, 3, 4, 5, 6, 7, 8
(B) {1, 2, 3, 4, 5, 6, 7,
(C) {2, 3, 4, 5, 6, 7, 8, 9,
(D) none of the above

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Q. The fuzzy proposition "IF X is E then Y is F" is a

(A) conditional unqualifi
(B) unconditional unq
(C) conditional qualifie
(D) unconditional qualified

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Q. Choose the correct statement
1. A fuzzy set is a crisp set but the reverse is not true
2. If A,B and C are three fuzzy sets defined over the same universe of discourse such that A ≤ B and B ≤ C and A ≤ C
3. Membership function defines the fuzziness in a fuzzy set irrespecive of the elements in the set, which are discrete or continuous

(A) 1 only
(B) 2 and 3
(C) 1,2 and 3
(D) none of these

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Q. An equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is

(A) fuzzy ≈ prediction
(B) fuzzy ≈ forecastin
(C) probability ≈ foreca
(D) none of these

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Q. Both fuzzy logic and artificial neural network are soft computing techniques because

(A) both gives precise an
(B) ann gives accura
(C) in each, no precise
(D) fuzzy gives exact resul

Computer Engineering Soft Computing MCQs with answers Page - 11

Q. The values of the set membership is represented by

Q. Given U = {1,2,3,4,5,6,7} A = {(3, 0.7), (5, 1), (6, 0.8)} then A will be: (where ~ → complement)

Q. If A and B are two fuzzy sets with membership functions
μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8}
μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5}
Then the value of μ(A∪B)’(x) will be

Q. Compute the value of adding the following two fuzzy integers:
A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)}
Where fuzzy addition is defined as
μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to

Q. A U (B U C) =

Q. Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction μA(x) = x / (x+2) Then the α cut corresponding to α = 0.5 will be

Q. The fuzzy proposition "IF X is E then Y is F" is a

Q. An equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is

Q. Both fuzzy logic and artificial neural network are soft computing techniques because

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Computer Engineering Soft Computing MCQs with answers Page - 11

Q. The values of the set membership is represented by

Q. Given U = {1,2,3,4,5,6,7} A = {(3, 0.7), (5, 1), (6, 0.8)} then A will be: (where ~ → complement)

Q. If A and B are two fuzzy sets with membership functions μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8} μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5} Then the value of μ(A∪B)’(x) will be

Q. Compute the value of adding the following two fuzzy integers: A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)} Where fuzzy addition is defined as μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to

Q. A U (B U C) =

Q. Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction μA(x) = x / (x+2) Then the α cut corresponding to α = 0.5 will be

Q. The fuzzy proposition "IF X is E then Y is F" is a

Q. An equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is

Q. Both fuzzy logic and artificial neural network are soft computing techniques because

Add MCQ in this Category

Browse Categories

Q. If A and B are two fuzzy sets with membership functions
μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8}
μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5}
Then the value of μ(A∪B)’(x) will be

Q. Compute the value of adding the following two fuzzy integers:
A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)}
Where fuzzy addition is defined as
μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to