Q.1.
Fuzzy Inference Method Was Designed To Attempt What?
Q.2.
Fuzzy set wherein no membership function has its value equal to 1 is called
Q.3.
X is A and y is B then z=c (c is constant), is
Q.4.
Disjuctives antecedents is method of ----- in FLC
Q.5.
Conjuctives antecedents is method of ----- in FLC
Q.6.
Fuzzy propositions, ---- gives an approximate idea of the number of elements of a subset fulfilling certain conditions
Q.7.
Defines logic funtion of two prepositions
Q.8.
Fuzzy set whose membership function has at least one element x in the universe whose membership valueis unity is called
Q.9.
Fuzzy logic and artificial neural network are soft computing techniques because
Q.10.
Equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is
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
Q.12.
Fuzzy proposition "IF X is E then Y is F" is a
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
Q.14.
U (B U 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
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
Q.17.
U = {1,2,3,4,5,6,A = {(0.7), (1), (0.8)}then A will be: (where ~ → complement)
Q.18.
Values of the set membership is represented by
Q.19.
What purpose Feedback neural networks are primarily used?
Q.20.
In the neural networks can perform what kind of operations?