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Welcome to the Fuzzy Systems MCQs Page

Dive deep into the fascinating world of Fuzzy Systems with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Fuzzy Systems, a crucial aspect of UGC CBSE NET Exam. In this section, you will encounter a diverse range of MCQs that cover various aspects of Fuzzy Systems, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within UGC CBSE NET Exam.

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Check out the MCQs below to embark on an enriching journey through Fuzzy Systems. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of UGC CBSE NET Exam.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Fuzzy Systems. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Fuzzy Systems MCQs | Page 1 of 3

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Q1.
Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction
 μA(x) = x / (x+2)
Then the α cut corresponding to α = 0.5 will be

a.

{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

b.

{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

c.

{2, 3, 4, 5, 6, 7, 8, 9, 10}

d.

None of the above

Discuss
Answer: (c).{2, 3, 4, 5, 6, 7, 8, 9, 10}
Q2.
If A and B are two fuzzy sets with membership functions:

μa(χ) ={0.2,0.5.,0.6,0.1,0.9}

μb (χ)= {0.1,0.5,0.2,0.7,0.8}

then the value of μa ∩ μb  will be

a.

{0.2,0.5,0.6,0.7,0.9}

b.

{0.2, 0.5,0.2, 0.1,0.8}

c.

{0.1, 0.5, 0.6, 0.1,0.8}

d.

{0.1, 0.5, 0.2, 0.1,0.8}

Discuss
Answer: (d).{0.1, 0.5, 0.2, 0.1,0.8}
Q3.
The height h(A) of a fuzzy set A is defined as

h(A) = sup A(x)

a.

h(A) = 0

b.

h(A) <0

c.

h(A)=1

d.

h(A)<1

Discuss
Answer: (c).h(A)=1
Q4.
A __________ point of a fuzzy set A is a point x ∈ X at which µA(x) = 0.5

a.

Core

b.

Support

c.

Cross-over

d.

α - cut

Discuss
Answer: (c).Cross-over
Q5.
Suppose the function y and a fuzzy integer number around -4 for x are given as y= (x-3)2 + 2.

Around -4 = {(2, 0.3), (3, 0.6), (4, 1), (5, 0.6), (6, 0.3)} respectively. Then f (Around -4) is given by:

a.

{(2, 0.6), (3, 0.3), (6, 1), (11, 0.3)}

b.

{(2, 0.6), (3, 1), (6, 1), (11, 0.3)}

c.

{(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)}

d.

{(2, 0.6), (3, 0.3), (6, 0.6), (11, 0.3)}

Discuss
Answer: (c).{(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)}
Q6.
Given  U =  {1,2,3,4,5,6,7}
 
             A = {(3, 0.7), (5, 1), (6, 0.8)}
 
then A will be: (where ~ → complement)

a.

{(4, 0.7), (2,1), (1,0.8)}

b.

{(4, 0.3.): (5, 0), (6. 0.2) }

c.

{(l, 1), (2, 1), (3, 0.3), (4, 1), (6,0.2), (7, 1)}

d.

{(3, 0.3), (6.0.2)}

Discuss
Answer: (c).{(l, 1), (2, 1), (3, 0.3), (4, 1), (6,0.2), (7, 1)}
Q7.
Consider a fuzzy set old as defined below
 
Old = {(20, 0.1), (30, 0.2), (40, 0.4), (50, 0.6), (60, 0.8), (70, 1), (80, 1)}
 
Then the alpha-cut for alpha = 0.4 for the set old will be

a.

{(40,0.4)}

b.

{50, 60, 70, 80}

c.

{(20, 0.1), (30, 0.2)}

d.

{(20,  0),  (30,  0),  (40,   1), (50,1), (60, 1), (70, 1), (80, 1)}

Discuss
Answer: (d).{(20,  0),  (30,  0),  (40,   1), (50,1), (60, 1), (70, 1), (80, 1)}
Q8.
Perceptron learning, Delta learning and LMS learning are learning methods which falls under the category of

a.

Error correction learning  -  learning with a teacher

b.

Reinforcement learning - learning with a critic

c.

Hebbian learning

d.

Competitive learning - learning without a teacher

Discuss
Answer: (a).Error correction learning  -  learning with a teacher
Q9.
Let R and S be two fuzzy relations defined as follows. Then, the resulting relation, T, which relates elements of universe x to the elements of universe z using max-min composition is given by:

a.

(1)

b.

(2)

c.

(3)

d.

(4)

Discuss
Answer: (c).(3)
Q10.
What are the following sequence of steps taken in designing a fuzzy logic machine ?

a.

Fuzzification → Rule evaluation → Defuzzification

b.

Fuzzification → Defuzzification → Rule evaluation

c.

Rule evaluation → Fuzzification → Defuzzification

d.

Rule evaluation → Defuzzification → Fuzzification

Discuss
Answer: (a).Fuzzification → Rule evaluation → Defuzzification
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