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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 3 of 9

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Q21.
In determination of weights by learning, for linear 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: (b).widrow learning law
Q22.
In determination of weights by learning, for noisy 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: (d).no learning law
Q23.
What are the features that can be accomplished using affine transformations?

a.

arbitrary rotation

b.

scaling

c.

translation

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q24.
What is the features that cannot be accomplished earlier without affine transformations?

a.

arbitrary rotation

b.

scaling

c.

translation

d.

all of the mentioned

Discuss
Answer: (c).translation
Q25.
What are affine transformations?

a.

addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern

b.

addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern

c.

addition of bias term (-1) or (+1) which results in arbitrary rotation, scaling, translation of input pattern

d.

none of the mentioned

Discuss
Answer: (a).addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern
Q26.
Can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Q27.
By using only linear processing units in output layer, can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q28.
Number of output cases depends on what factor?

a.

number of inputs

b.

number of distinct classes

c.

total number of classes

d.

none of the mentioned

Discuss
Answer: (b).number of distinct classes
Q29.
For noisy input vectors, Hebb methodology of learning can be employed?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q30.
What is the objective of perceptron learning?

a.

class identification

b.

weight adjustment

c.

adjust weight along with class identification

d.

none of the mentioned

Discuss
Answer: (c).adjust weight along with class identification
Page 3 of 9

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