Neural Network Questions and Answers – Applications of Neural Networks – 1

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This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Applications Of Neural Networks – 1″.

1. Which application out of these of robots can be made of single layer feedforward network?
a) wall climbing
b) rotating arm and legs
c) gesture control
d) wall following
View Answer

Answer: d
Explanation: Wall folloing is a simple task and doesn’t require any feedback.
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2. Which is the most direct application of neural networks?
a) vector quantization
b) pattern mapping
c) pattern classification
d) control applications
View Answer

Answer: c
Explanation: Its is the most direct and multilayer feedforward networks became popular because of this.

3. What are pros of neural networks over computers?
a) they have ability to learn b examples
b) they have real time high computational rates
c) they have more tolerance
d) all of the mentioned
View Answer

Answer: d
Explanation: Because of their parallel structure, they have high computational rates than conventional computers, so all are true.

4. what is true about single layer associative neural networks?
a) performs pattern recognition
b) can find the parity of a picture
c) can determine whether two or more shapes in a picture are connected or not
d) none of the mentioned
View Answer

Answer: a
Explanation: It can only perform pattern recognition, rest is not true for a single layer neural.

5. which of the following is false?
a) neural networks are artificial copy of the human brain
b) neural networks have high computational rates than conventional computers
c) neural networks learn by examples
d) none of the mentioned
View Answer

Answer: d
Explanation: All statements are true for a neural network.
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6. For what purpose, hamming network is suitable?
a) classification
b) association
c) pattern storage
d) none of the mentioned
View Answer

Answer: a
Explanation: Hamming network performs template matching between stored templates and inputs.

7. What happens in upper subnet of the hamming network?
a) classification
b) storage
c) output
d) none of the mentioned
View Answer

Answer: d
Explanation: In upper subnet, competitive interaction among units take place.

8. The competition in upper subnet of hamming network continues till?
a) only one unit remains negative
b) all units are destroyed
c) output of only one unit remains positive
d) none of the mentioned
View Answer

Answer: c
Explanation: The competition in upper subnet of hamming network continues till output of only one unit remains positive.

9. What does the activation value of winner unit is indicative of?
a) greater the degradation more is the activation value of winning units
b) greater the degradation less is the activation value of winning units
c) greater the degradation more is the activation value of other units
d) greater the degradation less is the activation value of other units
View Answer

Answer: b
Explanation: Simply, greater the degradation less is the activation value of winning units.

10. What does the matching score at first layer in recognition hamming network is indicative of?
a) dissimilarity of input pattern with patterns stored
b) noise immunity
c) similarity of input pattern with patterns stored
d) none of the mentioned
View Answer

Answer: c
Explanation: Matching score is simply a indicative of similarity of input pattern with patterns stored.
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Sanfoundry Global Education & Learning Series – Neural Networks.

To practice all areas of Neural Networks, here is complete set on 1000+ Multiple Choice Questions and Answers.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn