MCQOPTIONS
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MCQOPTIONS
This section includes 14 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. |
In competitive learning, node with highest activation is the winner, is it true? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 2. |
Generally how many kinds of pattern storage network exist? |
| A. | 2 |
| B. | 3 |
| C. | 4 |
| D. | 5 |
| Answer» C. 4 | |
| 3. |
What is the nature of weights in plain hebbian learning? |
| A. | convergent |
| B. | divergent |
| C. | may be convergent or divergent |
| D. | none of the mentioned |
| Answer» C. may be convergent or divergent | |
| 4. |
The weight change in plain hebbian learning is? |
| A. | 0 |
| B. | 1 |
| C. | 0 or 1 |
| D. | none of the mentioned |
| Answer» E. | |
| 5. |
GENERALLY_HOW_MANY_KINDS_OF_PATTERN_STORAGE_NETWORK_EXIST??$ |
| A. | 2 |
| B. | 3 |
| C. | 4 |
| D. | 5 |
| Answer» C. 4 | |
| 6. |
In competitive learning, node with highest activation is the winner, is it true?$ |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 7. |
what kind of feedbacks are given in competitive layer? |
| A. | self excitatory to self and others |
| B. | inhibitory to self and others |
| C. | self excitatory to self and inhibitory to others |
| D. | inhibitory to self and excitatory to others |
| Answer» D. inhibitory to self and excitatory to others | |
| 8. |
What is the other name of feedback layer in competitive neural networks? |
| A. | feedback layer |
| B. | feed layer |
| C. | competitive layer |
| D. | no such name exist |
| Answer» D. no such name exist | |
| 9. |
What is ojas rule? |
| A. | finds a unit weight vector |
| B. | maximises the mean squared output |
| C. | minimises the mean squared output |
| D. | none of the mentioned |
| Answer» E. | |
| 10. |
By normalizing the weight at every stage can we prevent divergence? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 11. |
How can divergence be prevented? |
| A. | using hopfield criteria |
| B. | sangers rule |
| C. | ojas rule |
| D. | sangers or ojas rule |
| Answer» E. | |
| 12. |
What is the nature of weights in plain hebbian learning? |
| A. | convergent |
| B. | divergent |
| C. | may be convergent or divergent |
| D. | none of the mentioned |
| Answer» C. may be convergent or divergent | |
| 13. |
The weight change in plain hebbian learning is? |
| A. | 0 |
| B. | 1 |
| C. | 0 or 1 |
| D. | none of the mentioned |
| Answer» E. | |
| 14. |
An instar can respond to a set of input vectors even if its not trained to capture the behaviour of the set? |
| A. | yes |
| B. | no |
| Answer» B. no | |