adplus-dvertising

Welcome to the Learning Concepts of Neural Networks MCQs Page

Dive deep into the fascinating world of Learning Concepts of Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Learning Concepts of Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Learning Concepts of 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.

frame-decoration

Check out the MCQs below to embark on an enriching journey through Learning Concepts of 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 Learning Concepts of Neural Networks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Learning Concepts of Neural Networks MCQs | Page 2 of 5

Explore more Topics under Neural Networks

Q11.
What was the 2nd stage in perceptron model called?

a.

sensory units

b.

summing unit

c.

association unit

d.

output unit

Discuss
Answer: (c).association unit
Q12.
What was the main deviation in perceptron model from that of MP model?

a.

more inputs can be incorporated

b.

learning enabled

c.

all of the mentioned

d.

none of the mentioned

Discuss
Answer: (b).learning enabled
Q13.
What is delta (error) in perceptron model of neuron?

a.

error due to environmental condition

b.

difference between desired and target output

c.

can be both due to difference in target output or environmental condition

d.

none of the mentioned

Discuss
Answer: (a).error due to environmental condition
Q14.
If a(i) is the input, ^ is the error, n is the learning parameter, then how can weight change in a perceptron model be represented?

a.

na(i)

b.

n^

c.

^a(i)

d.

none of the mentioned

Discuss
Answer: (d).none of the mentioned
Q15.
What is adaline in neural networks?

a.

adaptive linear element

b.

automatic linear element

c.

adaptive line element

d.

none of the mentioned

Discuss
Answer: (a).adaptive linear element
Q16.
Who invented the adaline neural model?

a.

Rosenblatt

b.

Hopfield

c.

Werbos

d.

Widrow

Discuss
Answer: (d).Widrow
Q17.
What was the main point of difference between the adaline and perceptron model?

a.

weights are compared with output

b.

sensory units result is compared with output

c.

analog activation value is compared with output

d.

all of the mentioned

Discuss
Answer: (c).analog activation value is compared with output
Q18.
In adaline model what is the relation between output and activation value(x)?

a.

linear

b.

nonlinear

c.

can be either linear or non-linear

d.

none of the mentioned

Discuss
Answer: (a).linear
Q19.
What is the another name of weight update rule in adaline model based on its functionality?

a.

LMS error learning law

b.

gradient descent algorithm

c.

both LMS error and gradient descent learning law

d.

none of the mentioned

Discuss
Answer: (c).both LMS error and gradient descent learning law
Q20.
In neural how can connectons between different layers be achieved?

a.

interlayer

b.

intralayer

c.

both interlayer and intralayer

d.

either interlayer or intralayer

Discuss
Answer: (c).both interlayer and intralayer
Page 2 of 5

Suggested Topics

Are you eager to expand your knowledge beyond Neural Networks? We've curated a selection of related categories that you might find intriguing.

Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!

Formal Languages and Automata Theory

Discover the mathematical models of computation with our Formal Languages and...

Cyber Security

Understand the fundamentals of safeguarding digital assets with our Cyber Security...

Internet of Things (IoT)

Discover how devices communicate with each other in the smart world with our IoT...

MATLAB

Hone your computational mathematics skills with our MATLAB MCQs. Understand...

PHP

Venture into server-side scripting with our PHP MCQs. Cover everything from syntax...

Artificial Intelligence

Get started on the path to the future with our Artificial Intelligence MCQs. Covering...

Systems Programming

Decode low-level programming with our Systems Programming MCQs. These questions delve...

Software Engineering

Learn about the systematic approach to developing software with our Software...

Microprocessor

Understand the heart of your computer with our Microprocessor MCQs. Topics include...