• #### 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

• 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
• 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

• 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
• 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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