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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 4 of 5

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Q31.
Which application out of these of robots can be made of single layer feedforward network?

a.

wall climbing

b.

rotating arm and legs

c.

gesture control

d.

wall following

Discuss
Answer: (d).wall following
Q32.
Which is the most direct application of neural networks?

a.

vector quantization

b.

pattern mapping

c.

pattern classification

d.

control applications

Discuss
Answer: (c).pattern classification
Q33.
What are pros of neural networks over computers?

a.

they have ability to learn b examples

b.

they have real time high computational rates

c.

they have more tolerance

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q34.
What is true about single layer associative neural networks?

a.

performs pattern recognition

b.

can find the parity of a picture

c.

can determine whether two or more shapes in a picture are connected or not

d.

none of the mentioned

Discuss
Answer: (a).performs pattern recognition
Q35.
Which of the following is false?

a.

neural networks are artificial copy of the human brain

b.

neural networks have high computational rates than conventional computers

c.

neural networks learn by examples

d.

none of the mentioned

Discuss
Answer: (d).none of the mentioned
Q36.
For what purpose, hamming network is suitable?

a.

classification

b.

association

c.

pattern storage

d.

none of the mentioned

Discuss
Answer: (a).classification
Q37.
What happens in upper subnet of the hamming network?

a.

classification

b.

storage

c.

output

d.

none of the mentioned

Discuss
Answer: (d).none of the mentioned
Q38.
The competition in upper subnet of hamming network continues till?

a.

only one unit remains negative

b.

all units are destroyed

c.

output of only one unit remains positive

d.

none of the mentioned

Discuss
Answer: (c).output of only one unit remains positive
Q39.
What does the activation value of winner unit is indicative of?

a.

greater the degradation more is the activation value of winning units

b.

greater the degradation less is the activation value of winning units

c.

greater the degradation more is the activation value of other units

d.

greater the degradation less is the activation value of other units

Discuss
Answer: (b).greater the degradation less is the activation value of winning units
Q40.
What does the matching score at first layer in recognition hamming network is indicative of?

a.

dissimilarity of input pattern with patterns stored

b.

noise immunity

c.

similarity of input pattern with patterns stored

d.

none of the mentioned

Discuss
Answer: (c).similarity of input pattern with patterns stored
Page 4 of 5

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