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 4 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

Q31.

On what factor the number of outputs depends?

distinct inputs

distinct classes

both on distinct classes and inputs

none of the mentioned

Discuss

Answer: (b).distinct classes

Q32.

In perceptron learning, what happens when input vector is correctly classified?

small adjustments in weight is done

large adjustments in weight is done

no adjustments in weight is done

weight adjustments doesn’t depend on classification of input vector

Discuss

Answer: (c).no adjustments in weight is done

Q33.

When two classes can be separated by a separate line, they are known as?

linearly separable

linearly inseparable classes

may be separable or inseparable, it depends on system

none of the mentioned

Discuss

Answer: (a).linearly separable

Q34.

If two classes are linearly inseparable, can perceptron convergence theorem be applied?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q35.

Two classes are said to be inseparable when?

there may exist straight lines that doesn’t touch each other

there may exist straight lines that can touch each other

there is only one straight line that separates them

all of the mentioned

Discuss

Answer: (c).there is only one straight line that separates them

Q36.

Is it necessary to set initial weights in prceptron convergence theorem to zero?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q37.

The perceptron convergence theorem is applicable for what kind of data?

binary

bipolar

both binary and bipolar

none of the mentioned

Discuss

Answer: (c).both binary and bipolar

Q38.

w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q39.

If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight.

e(m) = n(b(m) – s(m)) a(m)

e(m) = n(b(m) – s(m))

e(m) = (b(m) – s(m))

none of the mentioned

Discuss

Answer: (c).e(m) = (b(m) – s(m))

Q40.

Convergence in perceptron learning takes place if and only if:

a minimal error condition is satisfied

actual output is close to desired output

classes are linearly separable

all of the mentioned

Discuss

Answer: (c).classes are linearly separable

Page 4 of 9

Welcome to the Feedforward Neural Networks MCQs Page

Feedforward Neural Networks MCQs | Page 4 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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