Neural Networks MCQs on “Hopfield Model – 2”.
1. In hopfield network with symmetric weights, energy at each state may?
A. increase
B. decrease
C. decrease or remain same
D. decrease or increase
Answer: C
Clarification: Energy of the network cant increase as it may then lead to instability.
2. In hopfield model with symmetric weights, network can move to?
A. lower
B. higher
C. lower or higher
D. lower or same
Answer: D
Clarification: In hopfield model with symmetric weights, network can move to lower or same state.
3. Can error in recall due to false minima be reduced?
A. yes
B. no
Answer: A
Clarification: There are generally two methods to reduce error in recall due to false minima.
4. How can error in recall due to false minima be reduced?
A. deterministic update for states
B. stochastic update for states
C. not possible
D. none of the mentioned
Answer: B
Clarification: Error in recall due to false minima can be reduced by stochastic update for states.
5. Energy at each state in hopfield with symmetric weights network may increase or decrease?
A. yes
B. no
Answer: B
Clarification: Energy of the network cant increase as it may then lead to instability.
6. Pattern storage problem which cannot be represented by a feedback network of given size can be called as?
A. easy problems
B. hard problems
C. no such problem exist
D. none of the mentioned
Answer: B
Clarification: Pattern storage problem which cannot be represented by a feedback network of given size are known as hard problems.
7. What is the other way to reduce error in recall due to false minima apart from stochastic update?
A. no other method exist
B. by storing desired patterns at lowest energy minima
C. by storing desired patterns at energy maxima
D. none of the mentioned
Answer: B
Clarification: Error in recall due to false minima can be reduced by stochastic update or by storing desired patterns at lowest energy minima.
8. How can error in recall due to false minima be further reduced?
A. using suitable activation dynamics
B. cannot be further reduced
C. by storing desired patterns at energy maxima
D. none of the mentioned
Answer: A
Clarification: Error in recall due to false minima can further be reduced by using suitable activation dynamics.
9. As temperature increase, what happens to stochastic update?
A. increase in update
B. decrease in update
C. no change
D. none of the mentioned
Answer: C
Clarification: Temperature doesn’t effect stochastic update.
10. Why does change in temperature doesn’t effect stochastic update?
A. shape landscape depends on the network and its weights which varies accordingly and compensates the effect
B. shape landscape depends on the network and its weights which is fixed
C. shape landscape depends on the network, its weights and the output function which varies accordingly and compensates the effect
D. shape landscape depends on the network, its weights and the output function which is fixed
Answer: D
Clarification: Change in temperature doesn’t effect stochastic update because shape landscape depends on the network, its weights and the output function which is fixed.