Quiz  /  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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