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Welcome to the Feedback Neural Networks MCQs Page

Dive deep into the fascinating world of Feedback Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Feedback Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Feedback 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 Feedback Neural Networks. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Neural Networks.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Feedback Neural Networks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Feedback Neural Networks MCQs | Page 5 of 8

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Q41.
In hopfield network with symmetric weights, energy at each state may?

a.

increase

b.

decrease

c.

decrease or remain same

d.

decrease or increase

Discuss
Answer: (c).decrease or remain same
Q42.
In hopfield model with symmetric weights, network can move to?

a.

lower

b.

higher

c.

lower or higher

d.

lower or same

Discuss
Answer: (d).lower or same
Q43.
Can error in recall due to false minima be reduced?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Q44.
How can error in recall due to false minima be reduced?

a.

deterministic update for states

b.

stochastic update for states

c.

not possible

d.

none of the mentioned

Discuss
Answer: (b).stochastic update for states
Q45.
Energy at each state in hopfield with symmetric weights network may increase or decrease?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q46.
Pattern storage problem which cannot be represented by a feedback network of given size can be called as?

a.

easy problems

b.

hard problems

c.

no such problem exist

d.

none of the mentioned

Discuss
Answer: (b).hard problems
Q47.
What is the other way to reduce error in recall due to false minima apart from stochastic update?

a.

no other method exist

b.

by storing desired patterns at lowest energy minima

c.

by storing desired patterns at energy maxima

d.

none of the mentioned

Discuss
Answer: (b).by storing desired patterns at lowest energy minima
Q48.
How can error in recall due to false minima be further reduced?

a.

using suitable activation dynamics

b.

cannot be further reduced

c.

by storing desired patterns at energy maxima

d.

none of the mentioned

Discuss
Answer: (a).using suitable activation dynamics
Q49.
As temperature increase, what happens to stochastic update?

a.

increase in update

b.

decrease in update

c.

no change

d.

none of the mentioned

Discuss
Answer: (c).no change
Q50.
Why does change in temperature doesn’t effect stochastic update?

a.

shape landscape depends on the network and its weights which varies accordingly and compensates the effect

b.

shape landscape depends on the network and its weights which is fixed

c.

shape landscape depends on the network, its weights and the output function which varies accordingly and compensates the effect

d.

shape landscape depends on the network, its weights and the output function which is fixed

Discuss
Answer: (d).shape landscape depends on the network, its weights and the output function which is fixed
Page 5 of 8

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