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Soft computing Gate & PSU MCQ Questions With Answers
1.
Three main basic features involved in characterizing membership function are
Intution, Inference, Rank Ordering
Fuzzy Algorithm, Neural network, Genetic Algorithm
Core, Support , Boundary
Weighted Average, center of Sums, Median
2.
Core of soft Computing is....................
Fuzzy Computing, Neural Computing, Genetic Algorithms
Fuzzy Networks and Artificial Intelligence
Artificial Intelligence and Neural Science
Neural Science and Genetic Science
3.
Lotfi Zadeh is the father of
Fuzzy set
neural net
genetic algorithm
simulated annealing
4.
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) = max x + y = z(min (μA(x), μB(x))) Then, f (A + B) is equal to
{(0.5, 12), (0.6, 13), (1, 14), (0.7, 15), (0.7, 16), (1, 17), (1, 18)}
{(0.5, 12), (0.6, 13), (1, 14), (1, 15), (1, 16), (1, 17), (1, 18)}
{(0.3, 12), (0.5, 13), (0.5, 14), (1, 15), (0.7, 16), (0.5, 17), (0.2, 18)}
{(0.3, 12), (0.5, 13), (0.6, 14), (1, 15), (0.7, 16), (0.5, 17), (0.2, 18)}
5.
What Is Another Name For Fuzzy Inference Systems?
Fuzzy Expert System
Fuzzy Modelling
Fuzzy Logic Controller
All the Options
6.
Odd one out {2, 4, 7, 10, 0.4}
0.4
10
2
4
7.
Which of the following is not true regarding the principles of fuzzy logic ?
Fuzzy logic follows the principle of Aristotle and Buddha
Fuzzy logic is a concept of 'certain degree'
Japan is currently the most active users of fuzzy logic
Boolean logic is a subset of fuzzy logic
8.
What are the following sequence of steps taken in designing a fuzzy logic machine?
Fuzzification -> Rule Evaluation --> Defuzzification
Rule Evaluation -->Fuzzification ->Defuzzification
Defuzzification-->Rule Evaluation -->Fuzzification
Fuzzy Sets-->Defuzzification-->Rule Evaluation
9.
Neural Computing
mimics human brain
information processing paradigm
Both (a) and (b)
None of the above
10.
This is example for
fuzzy set representation
crisp set representation
universe representation
all three
11.
If the height of a membership function is 0.6, that is called
subnormal MF
Normal MF
Crossover point
none
12.
In a membership function which is has highest membership value
core
support
boundary
boundary and core
13.
Fuzzy logic is usually represented as
IF-THEN-ELSE rules
IF-THEN rules
Both IF-THEN-ELSE rules & IF-THEN rules
None of the mentioned
14.
This indicates
crisp set
fuzzy set
none
any one
15.
What Is Fuzzy Inference Systems?
The process of formulating the mapping from a given input to an output using fuzzy logic
Changing the output value to match the input value to give it an equal balance
Having a larger output than the input
Having a smaller output than the input
16.
Fuzzy set does not obey
Excluded middle and contradiction axioms
Excluded middle axiom
contradiction axiom
De Morgan's principles
17.
The membership functions are generally represented in
Tabular Form
Graphical Form
Mathematical Form
Logical Form
18.
Unsupervised learning is
learning without computers
problem based learning
learning from environment
learning from teachers
19.
Where is the minimum criterion used?
None of the options
in De Morgan's theorem
when there is an OR operation
when there is an AND operation
20.
Supervised Learning is
learning with the help of examples
learning without teacher
learning with the help of teacher
learning with computers as supervisor
21.
In a membership function which is has wide region
support
core
boundary
boundary and support
22.
Fuzzy Computing
mimics human behaviour
doesnt deal with 2 valued logic
deals with information which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic
All of the above
23.
A fuzzy set wherein no membership function has its value equal to 1 is called A.B.C.D.
normal fuzzy set
subnormal fuzzy set.
convex fuzzy set
concave fuzzy set
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