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.

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 4 of 5

Explore more Topics under Neural Networks

01

Basics of Artificial Neural Networks

02

Activation and Synaptic Dynamics

03

Feedforward Neural Networks

04

Feedback Neural Networks

05

Competitive Learning Neural Networks

06

Applications of Artificial Neural Networks

Q31.

On what parameters can change in weight vector depend?

learning parameters

input vector

learning signal

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q32.

If the change in weight vector is represented by ∆wij, what does it mean?

describes the change in weight vector for ith processing unit, taking input vector jth into account

describes the change in weight vector for jth processing unit, taking input vector ith into account

describes the change in weight vector for jth and ith processing unit.

none of the mentioned

Discuss

Answer: (a).describes the change in weight vector for ith processing unit, taking input vector jth into account

Q33.

What is learning signal in this equation ∆wij= µf(wi a)aj?

wi a

f(wi a)

Discuss

Answer: (d).f(wi a)

Q34.

State whether Hebb’s law is supervised learning or of unsupervised type?

supervised

unsupervised

either supervised or unsupervised

can be both supervised and unsupervised

Discuss

Answer: (b).unsupervised

Q35.

Hebb’s law can be represented by equation?

∆wij= µf(wi a)aj

∆wij= µ(si) aj, where (si) is output signal of ith input

both way

none of the mentioned

Discuss

Answer: (c).both way

Q36.

State which of the following statements hold foe perceptron learning law?

it is supervised type of learning law

it requires desired output for each input

∆wij= µ(bi – si) aj

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q37.

Delta learning is of unsupervised type?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q38.

Widrow and hoff learning law is special case of?

hebb learning law

perceptron learning law

delta learning law

none of the mentioned

Discuss

Answer: (c).delta learning law

Q39.

What’s the other name of widrow and hoff learning law?

Hebb

LMS

MMS

None of the mentioned

Discuss

Answer: (b).LMS

Q40.

Which of the following equation represent perceptron learning law?

∆wij= µ(si) aj

∆wij= µ(bi – si) aj

∆wij= µ(bi – si) aj Á(xi),wher Á(xi) is derivative of xi

∆wij= µ(bi – (wi a)) aj

Discuss

Answer: (b).∆wij= µ(bi – si) aj

Page 4 of 5

Welcome to the Learning Concepts of Neural Networks MCQs Page

Learning Concepts of Neural Networks MCQs | Page 4 of 5

Explore more Topics under Neural Networks

Basics of Artificial Neural Networks

Activation and Synaptic Dynamics

Feedforward Neural Networks

Feedback Neural Networks

Competitive Learning Neural Networks

Applications of Artificial Neural Networks

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