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Welcome to the Feedforward Neural Networks MCQs Page

Dive deep into the fascinating world of Feedforward Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Feedforward Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Feedforward 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 Feedforward 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 Feedforward Neural Networks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Feedforward Neural Networks MCQs | Page 8 of 9

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Q71.
What is true regarding backpropagation rule?

a.

it is also called generalized delta rule

b.

error in output is propagated backwards only to determine weight updates

c.

there is no feedback of signal at nay stage

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q72.
There is feedback in final stage of backpropagation algorithm?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q73.
What is meant by generalized in statement “backpropagation is a generalized delta rule” ?

a.

because delta rule can be extended to hidden layer units

b.

because delta is applied to only input and output layers, thus making it more simple and generalized

c.

it has no significance

d.

none of the mentioned

Discuss
Answer: (a).because delta rule can be extended to hidden layer units
Q74.
What are general limitations of back propagation rule?

a.

local minima problem

b.

slow convergence

c.

scaling

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q75.
What are the general tasks that are performed with backpropagation algorithm?

a.

pattern mapping

b.

function approximation

c.

prediction

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q76.
Does backpropagaion learning is based on gradient descent along error surface?

a.

YES

b.

NO

c.

cannot be said

d.

it depends on gradient descent but not error surface

Discuss
Answer: (a).YES
Q77.
How can learning process be stopped in backpropagation rule?

a.

there is convergence involved

b.

no heuristic criteria exist

c.

on basis of average gradient value

d.

none of the mentioned

Discuss
Answer: (c).on basis of average gradient value
Q78.
Which is a simplest pattern recognition task in a feedback network?

a.

heteroassociation

b.

autoassociation

c.

can be hetero or autoassociation, depends on situation

d.

none of the mentioned

Discuss
Answer: (b).autoassociation
Q79.
In a linear autoassociative network, if input is noisy than output will be noisy?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Q80.
Does linear autoassociative network have any practical use?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Page 8 of 9

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