Fuzzy Logic Artificial Intelligence UGC NET Questions And Answers
Fuzzy Logic Artificial Intelligence UGC NET Questions And Answers
Q1 ➡|Fuzzy Logic Artificial Intelligence Any soft‐computing methodology is characterized with
i ➥ Control action is formally defined
ii ➥ algorithm which can easily adapt with the change of dynamic environment
iii ➥precise solutions
iv ➥ control actions are unambiguous and accurate
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Answer: ii Explanation: algorithm which can easily adapt with the change of dynamic environment
Q2 ➡| Fuzzy LogicArtificial Intelligence A fuzzy set A is closed if:
i ➥
ii ➥
iii ➥
iv ➥
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Answer: ii Explanation:
Q3 ➡|Fuzzy LogicArtificial Intelligence The support of Fuzzy Set A is the set of all points x in X (is the universe of discourse) such that.
i ➥µA(x)>0
ii ➥ µA(x)=1
iii ➥µA(x)=0.5
iv ➥µA(x)≠1
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Answer: i Explanation:
Q4 ➡|Fuzzy LogicArtificial Intelligence An equivalence between Fuzzy vs. Probability to that of Prediction vs. Forecasting is (c)
i ➥ Fuzzy ≈ Forecasting
ii ➥ Probability ≈Forecasting
iii ➥Fuzzy ≈ Prediction
iv ➥None of the above
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Answer: i Explanation:
Q5 ➡|Fuzzy LogicArtificial Intelligence Both fuzzy logic and artificial neural network are soft computing techniques because
i ➥Fuzzy gives exact result but artificial neural network does not.
ii ➥ Both gives precise and accurate results.
iii ➥Artificial neural network gives accurate result, but fuzzy logic does not.
iv ➥In each, no precise mathematical model of the problem is required.
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Answer: iv Explanation:
Q6 ➡|Fuzzy LogicArtificial Intelligence Which of the following cannot be stated using fuzzy logic?
i ➥Date of birth of a student
ii ➥ Speed of a car
iii ➥Height of a person
iv ➥Color of an apple
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Answer: i Explanation:
Q7 ➡|Fuzzy LogicArtificial Intelligence Following which one is the example of Sigmoid Membership function?
i ➥
ii ➥
iii ➥
iv ➥
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Answer: iv Explanation:
Q8 ➡|Fuzzy LogicArtificial Intelligence How is Fuzzy Logic different from conventional control methods?
i ➥DO Approach
ii ➥ WHILE Approach
iii ➥FOR Approach
iv ➥IF and THEN Approach
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Answer: iv Explanation:
Q9 ➡|Fuzzy LogicArtificial Intelligence The height h(A) of a fuzzy set A is defined as h(A) = support A(x), where x belongs to A. Then the fuzzy set A is called normal when
i ➥ h(A)>1
ii ➥ h(A)=1
iii ➥h(A)<0
iv ➥h(A)=0
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Answer: ii Explanation:
Q10 ➡|Fuzzy LogicArtificial Intelligence Fuzzy logic is a form of
i ➥Binary set logic
ii ➥ Many‐valued logic
iii ➥Crisp set logic
iv ➥Two‐valued logic
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Answer: ii Explanation:
Q11 ➡|Fuzzy LogicArtificial Intelligence For k<1, which of the following concept can be used to generate other linguistic hedge.
i ➥Concentration
ii ➥ Concentration and Dilation
iii ➥Dilation
iv ➥None of the above
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Answer: i Explanation:
Q12 ➡|Fuzzy LogicArtificial Intelligence Given two fuzzy set A and B A = {(x1, 0.5), (x2, 0.1), (x3, 0.4)} And B = {(x1, 0.2), (x2, 0.3), (x3, 0.5)} Union of the two set, that is, A∪B is given by
i ➥{(x1, 0.2), (x2, 0.1), (x3, 0.4)}
ii ➥ {(x1, 0.2), (x2, 0.3), (x3, 0.5)}
iii ➥{(x1, 0.5), (x2, 0.3), (x3, 0.5)}
iv ➥{(x1, 0.5), (x2, 0.1), (x3, 0.4)}
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Answer: iii Explanation:
Q13 ➡|Fuzzy LogicArtificial Intelligence Given two Fuzzy Sets A and B with MFs µA and µB, respectively. Algebraic product or Vector product is given by:
i ➥max{0, μA(x) + μB(x)-1}
ii ➥ min{1, μA(x) + μB(x)}
iii ➥μA(x) + μB(x) – μA(x). μB(x)
iv ➥μA(x) ∙ μB(x)
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Answer: iv Explanation:
Q14 ➡|Fuzzy LogicArtificial Intelligence Two fuzzy sets A and B with membership functions µA(x) and µB(x), respectively defined as below. A = Hot Climate with µA(x) as the MF. B = Cold Climate with µB(x) as the MF Pleasant climate is given by:
i ➥min(µA(x), µB(x))
ii ➥ 1-µA(x)
iii ➥max(µA(x), µB(x))
iv ➥1-µB(x)
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Answer: i Explanation:
Q15 ➡|Fuzzy LogicArtificial Intelligence What is the Bandwidth of fuzzy set A which is given as follow? A = (10,0.1), (15,0.2), (20,0.5), (25,0.4), (30,0.4), (35,0.5), (40,0.2), (45,0.1)
