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 6 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

Q51.

What is a mapping problem?

when no restrictions such as linear separability is placed on the set of input – output pattern pairs

when there may be restrictions such as linear separability placed on input – output patterns

when there are restriction but other than linear separability

none of the mentioned

Discuss

Answer: (a).when no restrictions such as linear separability is placed on the set of input – output pattern pairs

Q52.

Can mapping problem be a more general case of pattern classification problem?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q53.

What is the objective of pattern mapping problem?

to capture weights for a link

to capture inputs

to capture feedbacks

to capture implied function

Discuss

Answer: (d).to capture implied function

Q54.

To provide generalization capability to a network, what should be done?

all units should be linear

all units should be non – linear

except input layer, all units in other layers should be non – linear

none of the mentioned

Discuss

Answer: (c).except input layer, all units in other layers should be non – linear

Q55.

Does an approximate system produce strictly an interpolated output?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q56.

The nature of mapping problem decides?

number of units in second layer

number of units in third layer

overall number of units in hidden layers

none of the mentioned

Discuss

Answer: (c).overall number of units in hidden layers

Q57.

How is hard learning problem solved?

using nonlinear differentiable output function for output layers

using nonlinear differentiable output function for hidden layers

using nonlinear differentiable output function for output and hidden layers

it cannot be solved

Discuss

Answer: (c).using nonlinear differentiable output function for output and hidden layers

Q58.

The number of units in hidden layers depends on?

the number of inputs

the number of outputs

both the number of inputs and outputs

the overall characteristics of the mapping problem

Discuss

Answer: (d).the overall characteristics of the mapping problem

Q59.

From given input-output pairs pattern recognition model should capture characteristics of the system?

True

False

May be True or False

Can't Say

Discuss

Answer: (a).True

Q60.

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: (b).problem is autoassociation

Page 6 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 6 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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