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 7 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

Q61.

Let a(l), b(l) represent in input-output pairs, where “l” varies in natural range of no.s, then if a(l)=!b(l)?

problem is heteroassociation

problem is autoassociation

can be either auto or heteroassociation

none of the mentioned

Discuss

Answer: (a).problem is heteroassociation

Q62.

The recalled output in pattern association problem depends on?

nature of input-output

design of network

both input and design

none of the mentioned

Discuss

Answer: (c).both input and design

Q63.

If a(l) gives output b(l) and a’=a(l)+m,where m is small quantity and if a’ gives ouput b(l) then?

network exhibits accretive behaviour

network exhibits interpolative behaviour

exhibits both accretive and interpolative behaviour

none of the mentioned

Discuss

Answer: (a).network exhibits accretive behaviour

Q64.

If a(l) gives output b(l) and a’=a(l)+m,where m is small quantity and if a’ gives ouput b(l)+n then?

network exhibits accretive behaviour

network exhibits interpolative behaviour

exhibits both accretive and interpolative behaviour

none of the mentioned

Discuss

Answer: (b).network exhibits interpolative behaviour

Q65.

Can system be both interpolative and accretive at same time?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q66.

What are 3 basic types of neural nets that form basic functional units?

i)feedforward
ii) loop
iii) recurrent
iv) feedback
v) combination of feed forward and back

i, ii, iii

i, ii, iv

i, iv, v

i, iii, v

Discuss

Answer: (c).i, iv, v

Q67.

Feedback networks are used for autoassociation and pattern storage?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q68.

Feedforward networks are also used for autoassociation and pattern storage?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q69.

What is the objective of backpropagation algorithm?

to develop learning algorithm for multilayer feedforward neural network

to develop learning algorithm for single layer feedforward neural network

to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

none of the mentioned

Discuss

Answer: (c).to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

Q70.

The backpropagation law is also known as generalized delta rule, is it true?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Page 7 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 7 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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