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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 1 of 8

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Q1.
How can false minima be reduced in case of error in recall in feedback neural networks?

a.

by providing additional units

b.

by using probabilistic update

c.

can be either probabilistic update or using additional units

d.

none of the mentioned

Discuss
Answer: (b).by using probabilistic update
Q2.
What is a Boltzman machine?

a.

A feedback network with hidden units

b.

A feedback network with hidden units and probabilistic update

c.

A feed forward network with hidden units

d.

A feed forward network with hidden units and probabilistic update

Discuss
Answer: (b).A feedback network with hidden units and probabilistic update
Q3.
What is objective of linear autoassociative feedforward networks?

a.

to associate a given pattern with itself

b.

to associate a given pattern with others

c.

to associate output with input

d.

none of the mentioned

Discuss
Answer: (a).to associate a given pattern with itself
Q4.
Is there any error in linear autoassociative networks?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q5.
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network?

a.

a(l)

b.

a(l) + e

c.

could be either a(l) or a(l) + e

d.

e

Discuss
Answer: (b).a(l) + e
Q6.
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is accretive in nature?

a.

a(l)

b.

a(l) + e

c.

could be either a(l) or a(l) + e

d.

e

Discuss
Answer: (a).a(l)
Q7.
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature?

a.

a(l)

b.

a(l) + e

c.

could be either a(l) or a(l) + e

d.

e

Discuss
Answer: (b).a(l) + e
Q8.
What property should a feedback network have, to make it useful for storing information?

a.

accretive behaviour

b.

interpolative behaviour

c.

both accretive and interpolative behaviour

d.

none of the mentioned

Discuss
Answer: (a).accretive behaviour
Q9.
What is the objective of a pattern storage task in a network?

a.

to store a given set of patterns

b.

to recall a give set of patterns

c.

both to store and recall

d.

none of the mentioned

Discuss
Answer: (c).both to store and recall
Q10.
Linear neurons can be useful for application such as interpolation, is it true?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Page 1 of 8

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