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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.

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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

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Q31.
On what parameters can change in weight vector depend?

a.

learning parameters

b.

input vector

c.

learning signal

d.

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?

a.

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

b.

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

c.

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

d.

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?

a.

µ

b.

wi a

c.

aj

d.

f(wi a)

Discuss
Answer: (d).f(wi a)
Q34.
State whether Hebb’s law is supervised learning or of unsupervised type?

a.

supervised

b.

unsupervised

c.

either supervised or unsupervised

d.

can be both supervised and unsupervised

Discuss
Answer: (b).unsupervised
Q35.
Hebb’s law can be represented by equation?

a.

∆wij= µf(wi a)aj

b.

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

c.

both way

d.

none of the mentioned

Discuss
Answer: (c).both way
Q36.
State which of the following statements hold foe perceptron learning law?

a.

it is supervised type of learning law

b.

it requires desired output for each input

c.

∆wij= µ(bi – si) aj

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q37.
Delta learning is of unsupervised type?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q38.
Widrow and hoff learning law is special case of?

a.

hebb learning law

b.

perceptron learning law

c.

delta learning law

d.

none of the mentioned

Discuss
Answer: (c).delta learning law
Q39.
What’s the other name of widrow and hoff learning law?

a.

Hebb

b.

LMS

c.

MMS

d.

None of the mentioned

Discuss
Answer: (b).LMS
Q40.
Which of the following equation represent perceptron learning law?

a.

∆wij= µ(si) aj

b.

∆wij= µ(bi – si) aj

c.

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

d.

∆wij= µ(bi – (wi a)) aj

Discuss
Answer: (b).∆wij= µ(bi – si) aj
Page 4 of 5

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