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 5 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

Q41.

When line joining any two points in the set lies entirely in region enclosed by the set in M-dimensional space , then the set is known as?

convex set

concave set

may be concave or convex

none of the mentioned

Discuss

Answer: (a).convex set

Q42.

Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q43.

If pattern classes are linearly separable then hypersurfaces reduces to straight lines?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q44.

As dimensionality of input vector increases, what happens to linear separability?

increases

decreases

no effect

doesn’t depend on dimensionality

Discuss

Answer: (b).decreases

Q45.

In a three layer network, shape of dividing surface is determined by?

number of units in second layer

number of units in third layer

number of units in second and third layer

none of the mentioned

Discuss

Answer: (a).number of units in second layer

Q46.

In a three layer network, number of classes is determined by?

number of units in second layer

number of units in third layer

number of units in second and third layer

none of the mentioned

Discuss

Answer: (b).number of units in third layer

Q47.

Intersection of linear hyperplanes in three layer network can only produce convex surfaces, is the statement true?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q48.

Intersection of convex regions in three layer network can only produce convex surfaces, is the statement true?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q49.

If the output produces nonconvex regions, then how many layered neural is required at minimum?

Discuss

Answer: (c).4

Q50.

Can all hard problems be handled by a multilayer feedforward neural network, with nonlinear units?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Page 5 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 5 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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