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This section includes 9 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Network knowledge and support exam preparation. Choose a topic below to get started.
1. |
How is pattern information distributed? |
A. | it is distributed all across the weights |
B. | it is distributed in localised weights |
C. | it is distributed in certain proctive weights only |
D. | none of the mentioned |
Answer» B. it is distributed in localised weights | |
2. |
Memory decay affects what kind of memory? |
A. | short tem memory in general |
B. | older memory in general |
C. | can be short term or older |
D. | none of the mentioned |
Answer» B. older memory in general | |
3. |
What are the requirements of learning laws? |
A. | convergence of weights |
B. | learning time should be as small as possible |
C. | learning should use only local weights |
D. | all of the mentioned |
Answer» E. | |
4. |
Learning is a? |
A. | slow process |
B. | fast process |
C. | can be slow or fast in general |
D. | can t say |
Answer» B. fast process | |
5. |
What is asynchronous update in a network? |
A. | update to all units is done at the same time |
B. | change in state of any one unit drive the whole network |
C. | change in state of any number of units drive the whole network |
D. | none of the mentioned |
Answer» C. change in state of any number of units drive the whole network | |
6. |
4.What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)? |
A. | xb(t)=0 |
B. | xb(t)=1 |
C. | xb(t)=n(t), where n is noise component |
D. | xb(t)=n(t)+1 |
Answer» D. xb(t)=n(t)+1 | |
7. |
What is equilibrium in neural systems? |
A. | deviation in present state, when small perturbations occur |
B. | settlement of network, when small perturbations occur |
C. | change in state, when small perturbations occur |
D. | none of the mentioned |
Answer» C. change in state, when small perturbations occur | |
8. |
If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by? |
A. | xb(t)=deterministic model |
B. | xb(t)=deterministic model + noise component |
C. | xb(t)=deterministic model*noise component |
D. | none of the mentioned |
Answer» C. xb(t)=deterministic model*noise component | |
9. |
Activation models are? |
A. | dynamic |
B. | static |
C. | deterministic |
D. | none of the mentioned |
Answer» D. none of the mentioned | |