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Quiz 2
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Q.1
Fuzzy Inference Method Was Designed To Attempt What?
control any two combinations of any two products by synthesising a set of linguistic control rules obtained from experienced human operations.
control any two combinations of any two products by synthesising a set of linguistic control rules obtained from experienced human operations.
control a steam engine and a boiler combination by synthesising a set of linguistic control rules obtained from experienced human operations.
control a air craft and fuel level combination by synthesising a set of linguistic control rules obtained from experienced human operations.
Q.2
Fuzzy set wherein no membership function has its value equal to 1 is called
normal fuzzy set
subnormal fuzzy set.
convex fuzzy set
concave fuzzy set
Q.3
X is A and y is B then z=c (c is constant), is
rule in zero order fis
rule in first order fis
both a and b
neither a nor b
Q.4
Disjuctives antecedents is method of ----- in FLC
decomposition rule
formation of rule
truth tables
all of the above
Q.5
Conjuctives antecedents is method of ----- in FLC
decomposition rule
formation of rule
truth tables
all of the above
Q.6
Fuzzy propositions, ---- gives an approximate idea of the number of elements of a subset fulfilling certain conditions
fuzzy predicate and predicate modifiers
fuzzy quantifiers
fuzzy qualifiers
all of the above
Q.7
Defines logic funtion of two prepositions
prepositions
lingustic hedges
truth tables
inference rules
Q.8
Fuzzy set whose membership function has at least one element x in the universe whose membership valueis unity is called
sub normal fuzzy sets
normal fuzzy set
convex fuzzy set
concave fuzzy set
Q.9
Fuzzy logic and artificial neural network are soft computing techniques because
both gives precise an
ann gives accura
in each, no precise
fuzzy gives exact resul
Q.10
Equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is
fuzzy ≈ prediction
fuzzy ≈ forecastin
probability ≈ foreca
none of these
Q.11
The correct statementA fuzzy set is a crisp set but the reverse is not trueIf A,B and C are three fuzzy sets defined over the same universe of discourse such that A ≤ B and B ≤ C and A ≤ CMembership function defines the fuzziness in a fuzzy set irrespecive of the elements in the set, which are discrete or continuous
1 only
2 and 3
1,2 and 3
none of these
Q.12
Fuzzy proposition "IF X is E then Y is F" is a
conditional unqualifi
unconditional unq
conditional qualifie
unconditional qualified
Q.13
A fuzzy set A defined on the interval X = [of integers by the membership JunctionμA(x) = x / (x+Then the α cut corresponding to α = 0.5 will be
{0, 1, 2, 3, 4, 5, 6, 7, 8
{1, 2, 3, 4, 5, 6, 7,
{2, 3, 4, 5, 6, 7, 8, 9,
none of the above
Q.14
U (B U C) =
(a ∩ b) ∩ (a ∩ c)
(a ∪ b ) ∪ c
(a ∪ b) ∩ (a ∪ c)
b ∩ a ∪ c
Q.15
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 asμA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to
{(0.5,12), (0.6,13), (1,
{(0.5,12), (0.6,13),
{(0.3,12), (0.5,13), (
{(0.3,12), (0.5,13), (0.6
Q.16
A and B are two fuzzy sets with membership functionsμA(x) = {0.0.0.0.0.8}μB(x) = {0.0.0.0.0.5}Then the value of μ(A∪B)’(x) will be
{0.9, 0.5, 0.6, 0.8, 0.8
{0.6, 0.2, 0.1, 0.7,
{0.1, 0.5, 0.4, 0.2, 0.
{0.1, 0.5, 0.4, 0.2, 0.3}
Q.17
U = {1,2,3,4,5,6,A = {(0.7), (1), (0.8)}then A will be: (where ~ → complement)
{(4, 0.7), (2,1), (1,0.8)
{(4, 0.3.): (5, 0), (6
{(l, 1), (2, 1), (3, 0.3)
{(3, 0.3), (6.0.2)}
Q.18
Values of the set membership is represented by
discrete set
degree of truth
probabilities
both degree of truth& probabilities
Q.19
What purpose Feedback neural networks are primarily used?
classification
feature mapping
pattern mapping
none of the mentioned
Q.20
In the neural networks can perform what kind of operations?
serial
parallel
serial or parallel
none of the mentioned
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