NLC GET Electrical Artificial Neural Networks MCQ PDF Part 1


NLC GET Electrical Artificial Neural Networks MCQ PDF Part 1

1.A perceptron is

A. a single layer feed-forward neural network with pre-processing
B. an auto-associative neural network
C. a double layer auto-associative neural network
D. a neural network that contains feedback

Answer-A

2.An auto-associative network is


A. a neural network that contains no loops
B. a neural network that contains feedback
C. a neural network that has only one loop
D. a single layer feed-forward neural network with pre-processing

Answer-B

3.Which of the following is true
(i) On average, neural networks have higher computational rates than conventional computers.
(ii) Neural networks learn by example.
(iii) Neural networks mimic the way the human brain works.

A. All of these
B. (ii) and (iii) are true
C. (i), (ii) and (iii) are true
D. None of these

Answer-A

4.Which of the following is true for neural networks
(i) The training time depends on the size of the network.
(ii) Neural networks can be simulated on a conventional computer.
(iii) Artificial neurons are identical in operation to biological ones.

A. All of these
B. (ii) is true
C. (i) and (ii) are true
D. None of these

Answer-C

5.What are the advantages of neural networks over conventional computers
(i) They have the ability to learn by example
(ii) They are more fault tolerant
(iii)They are more suited for real time operation due to their high ‘computational’ rates

A. (i) and (ii) are true
B. (i) and (iii) are true
C. Only (i)
D. All of these

Answer-D

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6.Which of the following is true
Single layer associative neural networks do not have the ability to
(i) perform pattern recognition
(ii) find the parity of a picture
(iii)determine whether two or more shapes in a picture are connected or not

A. (ii) and (iii) are true
B. (ii) is true
C. All of these
D. None of these

Answer-A

7.Which is true for neural networks

A. It has set of nodes and connections
B. Each node computes it’s weighted input
C. Node could be in excited state or non-excited state
D. All of these

Answer-D

8.Neuro software is

A. A software used to analyze neurons

B. It is powerful and easy neural network

C. Designed to aid experts in real world

D. It is software used by Neuro surgeon

Answer-B

9.Why is the XOR problem exceptionally interesting to neural network researchers

A. Because it can be expressed in a way that allows you to use a neural network
B. Because it is complex binary operation that cannot be solved using neural networks
C. Because it can be solved by a single layer perceptron
D. Because it is the simplest linearly inseparable problem that exists.

Answer-D

10.What is back propagation

A. It is another name given to the curvy function in the perceptron
B. It is the transmission of error back through the network to adjust the inputs
C. It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
D. None of these

Answer-C

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11.Why are linearly separable problems of interest of neural network researchers

A. Because they are the only class of problem that network can solve successfully
B. Because they are the only class of problem that Perceptron can solve successfully
C. Because they are the only mathematical functions that are continue
D. Because they are the only mathematical functions you can draw

Answer-D

12.Which of the following is not the promise of artificial neural network

A. It can explain result
B. It can survive the failure of some nodes
C. It has inherent parallelism
D. It can handle noise

Answer-A

13.Neural Networks are complex ______________ with many parameters

A. Linear Functions
B. Nonlinear Functions
C. Discrete Functions
D. Exponential Functions

Answer-A

14.A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.

A. True
B. False
C. Sometimes – it can also output intermediate values as well
D. Can’t say

Answer-A

15.The name for the function in question 16 is

A. Step function
B. Heaviside function
C. Logistic function
D. Perceptron function

Answer-B

16.The network that involves backward links from output to the input and hidden layers is called as____.

A. Self organizing maps
B. Perceptrons
C. Recurrent neural network
D. Multi layered perceptron

Answer-C

17.Which of the following is an application of NN (Neural Network)

A. Sales forecasting
B. Data validation
C. Risk management
D. All of these

Answer-D

18.Different learning method does not include

A. Memorization
B. Analogy
C. Deduction
D. Introduction

Answer-D

19.Which of the following is the model used for learning

A. Decision trees
B. Neural networks
C. Propositional and FOL rules
D. All of these

Answer-D

20.Automated vehicle is an example of ______

A. Supervised learning
B. Unsupervised learning
C. Active learning
D. Reinforcement learning

Answer-A

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