Introduction to Soft Computing-Multiple choice questions

1. SOFT COMPUTING
Important MCQs
For
Online Exam

2. 1) Which of the following is associated with fuzzy logic?
a. Crisp set logic
b. Many-valued logic
c. Two-valued logic
d. Binary set logic
Answer: b) Many-valued logic
Explanation: Since fuzzy logic can define the set membership with some specific
value, it may have multiple set values.
2) The truth values of traditional set theory can be defined as _________ and that
of fuzzy logic is termed as _________.
a. Either 0 or 1, either 0 or 1.
b. Between 0 & 1, either 0 or 1.
c. Either 0 or 1, between 0 & 1.
d. Between 0 & 1, between 0 & 1.
Answer: c) Either 0 or 1, between 0 & 1.
Explanation: A crisp set is usually defined by crisp boundaries containing the
precise location of the set boundaries.
However, a fuzzy set is defined by the indeterminate boundaries containing
uncertainty about the set's boundaries.

3. 4) A Fuzzy logic is an extension to the Crisp set, which handles the Partial Truth.
a. True
b. False
Answer: a) True.
Explanation: None.
3) How many types of random variables are there in Fuzzy logic?
a.2
b.4
c.1
d.3
Answer: d) 3
Explanation: There are three types of random variables, i.e., Boolean, discrete, and
continuous variables.

4. 5) Which of the following represents the values of set membership?
a. Degree of truth
b. Probabilities
c. Discrete set
d. Both a & b
Answer: a) Degree of truth
Explanation: Both probabilities and degree of truth range between 0 and 1.
6) Which of the following fuzzy operators are utilized in fuzzy set theory?
a. AND
b. OR
c. NOT
d. EX-OR
Answer: a), b) and c)
Explanation: In fuzzy logic, the AND, OR, and NOT operators represent the
minimum, maximum, and complement.

5. 7) _________ represents the fuzzy logic
a. IF-THEN rules
b. IF-THEN-ELSE rules
c. Both a & b
d. None of the above
Answer: a) IF-THEN rules
Explanation: In fuzzy set theory, the fuzzy operators are defined on the fuzzy
sets. When the fuzzy operators are anonymous, the fuzzy logic utilizes the IF-
THEN rules.
In general, rules are expressed as:
IF variable IS property THEN action
8) Uncertainty can be represented by _________
a. Entropy
b. Fuzzy logic
c. Probability
d. All of the above
Answer: d) All of the above
Explanation: Entropy is the amount of uncertainty involved in data,
which is represented by H(data).

6. 9) A perceptron can be defined as _________
1.A double layer auto-associative neural network
2.A neural network with feedback
3.An auto-associative neural network
4.A single layer feed-forward neural network with pre-processing
Answer: d) A single layer feed-forward neural network with pre-
processing
Explanation: A perceptron is a single-layer neural network that
consists of input values, weights, bias, net sum followed by an
activation function.

7. 10) What is meant by an auto-associative neural network?
a. A neural network including feedback
b. A neural network containing no loops
c. A neural network having a single loop
d. A single layer feed-forward neural network containing feedback
Answer: a) A neural network including feedback
Explanation: Auto associative networks are yet another kind of
feed-forward nets trained to estimate the identity matrix in
between network inputs and outputs by incorporating back
propagation.

8. 11) Which of the following is correct?
I. In contrast to conventional computers, neural networks have much higher
computational rates.
II. Neural networks learn by example.
III. Neural networks mimic the same way as that of the human brain
a. All of the above
b. (ii) and (iii) are true
c. (i), (ii) and (iii) are true
d. None of the above
Answer: a) All of the above
Explanation: Neural networks can run multiple operations in parallel, which is
why they have higher computational rates than conventional computers. Neural
nets mimic the working of the human brain. The idea behind neural nets is not to
be programmed but to learn by examples.

9. 12) Which of the following is correct for the neural network?
I. The training time is dependent on the size of the network
II. Neural networks can be simulated on the conventional computers
III. Artificial neurons are identical in operation to a biological one
a. All of the above
b. (ii) is true
c. (i) and (ii) are true
d. None of the above
Answer: c) (i) and (ii) are true
Explanation: The training time depends on the network size; the more the
number of neurons, the more would be the possible states. Neural networks can
be simulated on a conventional computer, but neural networks' main advantage -
parallel execution - is lost. Artificial neurons are not identical in operation to
biological ones.

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