# Soft Computing MCQs

## MCQs on Soft Computing

Solve Soft Computing Multiple-Choice Questions to prepare better for GATE. Learn more about Soft Computing and Soft Computing MCQs by checking notes, mock tests, and previous years’ question papers. Gauge the pattern of MCQs on Soft Computing by solving the ones that we have compiled below for your practice:

## Soft Computing Multiple-Choice Questions

1. Which of these has been associated with fuzzy logic?

a. Many-valued logic

b. Crisp set logic

c. Binary set logic

d. Two-valued logic

2. How is the probability density function represented?

a. Probability distributions

b. Probability distributions for the Continuous variables

c. Discrete variable

d. Continuous variable

Answer: (b) Probability distributions for the Continuous variables

3. How can uncertainty be represented?

a. Fuzzy logic

b. Probability

c. Entropy

d. All of the above

Answer: (d) All of the above

4. The name of the operator that is present in fuzzy set theory, that is linguistic in nature, is:

a. Hedges

b. Lingual Variable

c. Fuzz Variable

d. All of the above

5. Which of these conditions can influence a variable directly by all the others?

a. Locally connected

b. Partially connected

c. Fully connected

d. All of the above

6. Which of these is NOT an artificial neural network’s promise?

a. It is capable of handling noise

b. It is capable of surviving the failure of some nodes

c. It is capable of inherent parallelism

d. It is capable of explaining the result

Answer: (d) It is capable of explaining the result

7. We can use the membership function to solve empirical problems based on:

a. Examples

b. Experience

c. Learning

d. Knowledge

8. A given 4-input neuron weighs 1, 2, 3, 4. The transfer function here is linear, and the constant of proportionality is equivalent to 2. Also, the inputs here are 4, 10, 5, 20, respectively. Thus, the output would be:

a. 119

b. 123

c. 238

d. 76

9. What would be the name of a network that includes backward links from a given output to its inputs along with the hidden layers?

a. Recurrent neural network

b. Multi-layered perceptron

c. Self-organising maps

d. Perceptron

10. What out of these is involved in the case of inductive learning?

a. Irregular Hypothesis

b. Estimated Hypothesis

c. Consistent Hypothesis

d. Inconsistent Hypothesis

11. Which of these is not counted in various learning methods?

a. Deduction

b. Introduction

c. Memorisation

d. Analogy

12. An automated vehicle refers to an application of which of these?

a. Reinforcement learning

b. Unsupervised learning

c. Active learning

d. Supervised learning

13. Which of these is termed to be exploratory learning?

a. Unsupervised learning

b. Reinforcement learning

c. Supervised learning

d. Active learning

14. What is the feature of ANN in which the ANN would create its own organisation for the representation of all the information that it receives during its learning time?

a. Supervised Learning

b. Self-Organisation

c. What-if Analysis

15. Which of these would take input in the form of an object that is described by an attribute set?

a. Decision graph

b. Graph

c. Decision tree

d. Tree

16. Every connection link present in ANN gets linked to the ________ that consists of various statics about an input signal.

a. Activation function

b. Neurons

c. Bias

d. Weights

17. A crossover operator proceeds in how many steps?

a. 5

b. 4

c. 3

d. 2

18. Which of these would help in the conversion of a bit pattern into some other bit pattern with the help of a logical bitwise operation?

a. Segregation

c. Inversion

d. Conversion

19. Which of these is NOT a specified method that is used for the selection of the parents?

b. Tournament Selection

c. Boltzmann selection

d. Elitism

20. The interconnected processing elements in an artificial neural network are known as:

a. Soma

b. Axon

c. Weights

d. Neurons or Nodes