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 8 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

Q71.

What is true regarding backpropagation rule?

it is also called generalized delta rule

error in output is propagated backwards only to determine weight updates

there is no feedback of signal at nay stage

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q72.

There is feedback in final stage of backpropagation algorithm?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q73.

What is meant by generalized in statement “backpropagation is a generalized delta rule” ?

because delta rule can be extended to hidden layer units

because delta is applied to only input and output layers, thus making it more simple and generalized

it has no significance

none of the mentioned

Discuss

Answer: (a).because delta rule can be extended to hidden layer units

Q74.

What are general limitations of back propagation rule?

local minima problem

slow convergence

scaling

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q75.

What are the general tasks that are performed with backpropagation algorithm?

pattern mapping

function approximation

prediction

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q76.

Does backpropagaion learning is based on gradient descent along error surface?

YES

cannot be said

it depends on gradient descent but not error surface

Discuss

Answer: (a).YES

Q77.

How can learning process be stopped in backpropagation rule?

there is convergence involved

no heuristic criteria exist

on basis of average gradient value

none of the mentioned

Discuss

Answer: (c).on basis of average gradient value

Q78.

Which is a simplest pattern recognition task in a feedback network?

heteroassociation

autoassociation

can be hetero or autoassociation, depends on situation

none of the mentioned

Discuss

Answer: (b).autoassociation

Q79.

In a linear autoassociative network, if input is noisy than output will be noisy?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q80.

Does linear autoassociative network have any practical use?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Page 8 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 8 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

Suggested Topics

Compiler Design

Digital Image Processing (DIP)

MATLAB

Computer Hardware

MySQL Database

C# programming

R Programming

Discrete Mathematics

Web Technologies