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Welcome to the Applications of Artificial Neural Networks MCQs Page

Dive deep into the fascinating world of Applications of Artificial Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Applications of Artificial Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Applications of Artificial Neural Networks, 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 Neural Networks.

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Check out the MCQs below to embark on an enriching journey through Applications of Artificial Neural Networks. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Neural Networks.

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Applications of Artificial Neural Networks MCQs | Page 2 of 5

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Q11.
What is the use of MLFFNN?

a.

to realize structure of MLP

b.

to solve pattern classification problem

c.

to solve pattern mapping problem

d.

to realize an approximation to a MLP

Discuss
Answer: (d).to realize an approximation to a MLP
Q12.
What is the advantage of basis function over mutilayer feedforward neural networks?

a.

training of basis function is faster than MLFFNN

b.

training of basis function is slower than MLFFNN

c.

storing in basis function is faster than MLFFNN

d.

none of the mentioned

Discuss
Answer: (a).training of basis function is faster than MLFFNN
Q13.
Why is the training of basis function is faster than MLFFNN?

a.

because they are developed specifically for pattern approximation

b.

because they are developed specifically for pattern classification

c.

because they are developed specifically for pattern approximation or classification

d.

none of the mentioned

Discuss
Answer: (c).because they are developed specifically for pattern approximation or classification
Q14.
Pattern recall takes more time for?

a.

MLFNN

b.

Basis function

c.

Equal for both MLFNN and basis function

d.

None of the mentioned

Discuss
Answer: (b).Basis function
Q15.
In which type of networks training is completely avoided?

a.

GRNN

b.

PNN

c.

GRNN and PNN

d.

None of the mentioned

Discuss
Answer: (c).GRNN and PNN
Q16.
What does GRNN do?

a.

function approximation task

b.

pattern classification task

c.

function approximation and pattern classification task

d.

none of the mentioned

Discuss
Answer: (a).function approximation task
Q17.
What does PNN do?

a.

function approximation task

b.

pattern classification task

c.

function approximation and pattern classification task

d.

none of the mentioned

Discuss
Answer: (b).pattern classification task
Q18.
The CPN provides practical approach for implementing?

a.

patter approximation

b.

pattern classification

c.

pattern mapping

d.

pattern clustering

Discuss
Answer: (c).pattern mapping
Q19.
What consist of a basic counterpropagation network?

a.

a feedforward network only

b.

a feedforward network with hidden layer

c.

two feedforward network with hidden layer

d.

none of the mentioned

Discuss
Answer: (c).two feedforward network with hidden layer
Q20.
How does the name counterpropagation signifies its architecture?

a.

its ability to learn inverse mapping functions

b.

its ability to learn forward mapping functions

c.

its ability to learn forward and inverse mapping functions

d.

none of the mentioned

Discuss
Answer: (c).its ability to learn forward and inverse mapping functions
Page 2 of 5

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