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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 2 of 9

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Q11.
What are hard problems?

a.

classification problems which are not clearly separable

b.

classification problems which are not associatively separable

c.

classification problems which are not functionally separable

d.

none of the mentioned

Discuss
Answer: (d).none of the mentioned
Q12.
In order to overcome constraint of linearly separablity concept of multilayer feedforward net is proposed?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Q13.
The hard learning problem is ultimately solved by hoff’s algorithm?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q14.
Generalization feature of a multilayer feedforward network depends on factors?

a.

architectural details

b.

learning rate parameter

c.

training samples

d.

all of the mentioned

Discuss
Answer: (a).architectural details
Q15.
What is accretive behaviour?

a.

not a type of pattern clustering task

b.

for small noise variations pattern lying closet to the desired pattern is recalled.

c.

for small noise variations noisy pattern having parameter adjusted according to noise variation is recalled

d.

none of the mentioned

Discuss
Answer: (b).for small noise variations pattern lying closet to the desired pattern is recalled.
Q16.
What is Interpolative behaviour?

a.

not a type of pattern clustering task

b.

for small noise variations pattern lying closet to the desired pattern is recalled.

c.

for small noise variations noisy pattern having parameter adjusted according to noise variation is recalled

d.

none of the mentioned

Discuss
Answer: (c).for small noise variations noisy pattern having parameter adjusted according to noise variation is recalled
Q17.
Does pattern association involves non linear units in feedforward neural network?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q18.
What is the feature that doesn’t belongs to pattern classification in feeddorward neural networks?

a.

recall is direct

b.

delta rule learning

c.

non linear processing units

d.

two layers

Discuss
Answer: (b).delta rule learning
Q19.
What is the feature that doesn’t belongs to pattern mapping in feeddorward neural networks?

a.

recall is direct

b.

delta rule learning

c.

non linear processing units

d.

two layers

Discuss
Answer: (d).two layers
Q20.
In determination of weights by learning, for orthogonal input vectors what kind of learning should be employed?

a.

hebb learning law

b.

widrow learning law

c.

hoff learning law

d.

no learning law

Discuss
Answer: (a).hebb learning law
Page 2 of 9

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