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.

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

Explore more Topics under Neural Networks

01

Basics of Artificial Neural Networks

02

Learning Concepts of Neural Networks

03

Activation and Synaptic Dynamics

04

Feedback Neural Networks

05

Competitive Learning Neural Networks

06

Applications of Artificial Neural Networks

Q21.

In determination of weights by learning, for linear input vectors what kind of learning should be employed?

hebb learning law

widrow learning law

hoff learning law

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?

hebb learning law

widrow learning law

hoff learning law

no learning law

Discuss

Answer: (d).no learning law

Q23.

What are the features that can be accomplished using affine transformations?

arbitrary rotation

scaling

translation

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q24.

What is the features that cannot be accomplished earlier without affine transformations?

arbitrary rotation

scaling

translation

all of the mentioned

Discuss

Answer: (c).translation

Q25.

What are affine transformations?

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

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

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

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?

YES

May be YES or NO

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?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q28.

Number of output cases depends on what factor?

number of inputs

number of distinct classes

total number of classes

none of the mentioned

Discuss

Answer: (b).number of distinct classes

Q29.

For noisy input vectors, Hebb methodology of learning can be employed?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q30.

What is the objective of perceptron learning?

class identification

weight adjustment

adjust weight along with class identification

none of the mentioned

Discuss

Answer: (c).adjust weight along with class identification

Page 3 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 3 of 9

Explore more Topics under Neural Networks

Basics of Artificial Neural Networks

Learning Concepts of Neural Networks

Activation and Synaptic Dynamics

Feedback Neural Networks

Competitive Learning Neural Networks

Applications of Artificial Neural Networks

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