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This section includes 16 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Networks knowledge and support exam preparation. Choose a topic below to get started.
1. |
What does vigilance parameter in ART determines? |
A. | number of possible outputs |
B. | number of desired outputs |
C. | number of acceptable inputs |
D. | none of the mentioned |
Answer» E. | |
2. |
ART is made to tackle? |
A. | stability problem |
B. | hard problems |
C. | storage problems |
D. | none of the mentioned |
Answer» E. | |
3. |
A greater value of ‘p’ the vigilance parameter leads to? |
A. | small clusters |
B. | bigger clusters |
C. | no change |
D. | none of the mentioned |
Answer» B. bigger clusters | |
4. |
What type of inputs does ART – 1 receives? |
A. | bipolar |
B. | binary |
C. | both bipolar and binary |
D. | none of the mentiobned |
Answer» C. both bipolar and binary | |
5. |
What does ART stand for? |
A. | Automatic resonance theory |
B. | Artificial resonance theory |
C. | Adaptive resonance theory |
D. | None of the mentioned |
Answer» D. None of the mentioned | |
6. |
An auto – associative network is? |
A. | network in neural which contains feedback |
B. | network in neural which contains loops |
C. | network in neural which no loops |
D. | none of the mentioned |
Answer» B. network in neural which contains loops | |
7. |
ART_IS_MADE_TO_TACKLE??$ |
A. | stability problem |
B. | hard problems |
C. | storage problems |
D. | none of the mentioned |
Answer» E. | |
8. |
What_does_vigilance_parameter_in_ART_determines?$ |
A. | number of possible outputs |
B. | number of desired outputs |
C. | number of acceptable inputs |
D. | none of the mentioned |
Answer» E. | |
9. |
A greater value of ‘p’ the vigilance parameter leads to?# |
A. | small clusters |
B. | bigger clusters |
C. | no change |
D. | none of the mentioned |
Answer» B. bigger clusters | |
10. |
What type of inputs does ART – 1 receives?$ |
A. | bipolar |
B. | binary |
C. | both bipolar and binary |
D. | none of the mentiobned |
Answer» C. both bipolar and binary | |
11. |
hat type learning is involved in ART? |
A. | supervised |
B. | unsupervised |
C. | supervised and unsupervised |
D. | none of the mentioned |
Answer» C. supervised and unsupervised | |
12. |
What is the purpose of ART? |
A. | take care of approximation in a network |
B. | take care of update of weights |
C. | take care of pattern storage |
D. | none of the mentioned |
Answer» E. | |
13. |
What is the full form of ART in Art? |
A. | Automatic resonance theory |
B. | Artificial resonance theory |
C. | Adaptive resonance theory |
D. | None of the mentioned |
Answer» D. None of the mentioned | |
14. |
The bidirectional associative memory is similar in principle to? |
A. | hebb learning model |
B. | boltzman model |
C. | Papert model |
D. | none of the mentioned |
Answer» E. | |
15. |
What is true about sigmoidal neurons? |
A. | can accept any vectors of real numbers as input |
B. | outputs a real number between 0 and 1 |
C. | they are the most common type of neurons |
D. | all of the mentioned |
Answer» E. | |
16. |
An auto – associative network is? |
A. | network in neural which contains feedback |
B. | network in neural which contains loops |
C. | network in neural which no loops |
D. | none of the mentioned |
Answer» B. network in neural which contains loops | |