Category: Neural Networks Objective Questions

250+ MCQs on Hopfield Model-1 and Answers

Neural Networks Multiple Choice Questions on “Hopfield Model – 1″.

1. How can states of units be updated in hopfield model?
A. synchronously
B. asynchronously
C. synchronously and asynchronously
D. none of the mentioned
Answer: C
Clarification: States of units be updated synchronously and asynchronously in hopfield model.

2. What is synchronous update in hopfield model?
A. all units are updated simultaneously
B. a unit is selected at random and its new state is computed
C. a predefined unit is selected and its new state is computed
D. none of the mentioned
Answer: A
Clarification: In synchronous update, all units are updated simultaneously.

3. What is asynchronous update in hopfield model?
A. all units are updated simultaneously
B. a unit is selected at random and its new state is computed
C. a predefined unit is selected and its new state is computed
D. none of the mentioned
Answer: B
Clarification: In asynchronous update, a unit is selected at random and its new state is computed.

4. Asynchronous update ensures that the next state is atmost unit hamming distance from current state, is that true?
A. yes
B. no
Answer: A
Clarification: Asynchronous update ensures that the next state is at most unit hamming distance from current state.

5. If pattern is to be stored, then what does stable state should have updated value of?
A. current sate
B. next state
C. both current and next state
D. none of the mentioned
Answer: A
Clarification: Stable state should have updated value of current sate.

6. For symmetric weights there exist?
A. basins of attraction corresponding to energy minimum
B. false wells
C. fluctuations in energy landscape
D. none of he mentioned
Answer: A
Clarification: For symmetric weights there exist a stable point.

7. If connections are not symmetric then basins of attraction may correspond to?
A. oscillatory regions
B. stable regions
C. chaotic regions
D. oscillatory or chaotic regions
Answer: D
Clarification: If connections are not symmetric then basins of attraction may correspond to oscillatory or chaotic regions.

8. For analysis of storage capacity what are the conditions imposed on hopfield model?
A. symmetry of weights
B. asynchronous update
C. symmetry of weights and asynchronous update
D. none of the mentioned
Answer: C
Clarification: For analysis of storage capacity, symmetry of weights and asynchronous update conditions are imposed on hopfield model.

9. What is gradient descent?
A. method to find the absolute minimum of a function
B. method to find the absolute maximum of a function
C. maximum or minimum, depends on the situation
D. none of the mentioned
Answer: a
Clarification: Gradient descent gives absolute minimum of a function.

10. If connections are not symmetric then basins of attraction may correspond to oscillatory or stable regions, is that true?
A. yes
B. no
Answer: B
Clarification: Asymmetric weight can’t lead to stable regions.

250+ MCQs on Characteristics – 1 and Answers

Neural Networks Multiple Choice Questions on “Characteristics – 1″.

1. What are the issues on which biological networks proves to be superior than AI networks?
A. robustness & fault tolerance
B. flexibility
C. collective computation
D. all of the mentioned
Answer: D
Clarification: AI network should be all of the above mentioned.

2. The fundamental unit of network is
A. brain
B. nucleus
C. neuron
D. axon
Answer: C
Clarification: Neuron is the most basic & fundamental unit of a network .

3. What are dendrites?
A. fibers of nerves
B. nuclear projections
C. other name for nucleus
D. none of the mentioned
Answer: A
Clarification: Dendrites tree shaped fibers of nerves.

4. What is shape of dendrites like
A. oval
B. round
C. tree
D. rectangular
Answer: C
Clarification: Basic biological q&a.

5. Signal transmission at synapse is a?
A. physical process
B. chemical process
C. physical & chemical both
D. none of the mentioned
Answer: B
Clarification: Since chemicals are involved at synapse , so its an chemical process.

6. How does the transmission/pulse acknowledged ?
A. by lowering electric potential of neuron body
B. by raising electric potential of neuron body
C. both by lowering & raising electric potential
D. none of the mentioned
Answer: C
Clarification: There is equal probability of both.

7. When the cell is said to be fired?
A. if potential of body reaches a steady threshold values
B. if there is impulse reaction
C. during upbeat of heart
D. none of the mentioned
Answer: A
Clarification: Cell is said to be fired if & only if potential of body reaches a certain steady threshold values.

8. Where does the chemical reactions take place in neuron?
A. dendrites
B. axon
C. synapses
D. nucleus
Answer: C
Clarification: It is a simple biological fact.

9. Function of dendrites is?
A. receptors
B. transmitter
C. both receptor & transmitter
D. none of the mentioned
Answer: A
Clarification: Dendrites are tree like projections whose function is only to receive impulse.

10. What is purpose of Axon?
A. receptors
B. transmitter
C. transmission
D. none of the mentioned
Answer: C
Clarification: Axon is the body of neuron & thus cant be at ends of it so cant receive & transmit signals.

250+ MCQs on Stability & Convergence and Answers

Neural Networks Multiple Choice Questions on “Convergence & stability″.

1. Stability refers to adjustment in behaviour of weights during learning?
A. yes
B. no
Answer: B
Clarification: Stability refers to equilibrium behaviour of activation state.

2. Convergence refers to equilibrium behaviour of activation state?
A. yes
B. no
Answer: B
Clarification: Convergence refers to adjustment in behaviour of weights during learning.

3. What leads to minimization of error between the desired & actual outputs?
A. stability
B. convergence
C. either stability or convergence
D. none of the mentioned
Answer: B
Clarification: Convergence is responsible for minimization of error between the desired & actual outputs.

4. Stability is minimization of error between the desired & actual outputs?
A. yes
B. no
Answer: B
Clarification: Convergence is minimization of error between the desired & actual outputs.

5. How many trajectories may terminate at same equilibrium state?
A. 1
C. 2
C. many
D. none
Answer: C
Clarification: There may be several trajectories that may settle to same equilibrium state.

6. If weights are not symmetric i.e cik =! cki, then what happens?
A. network may exhibit periodic oscillations of states
B. no oscillations as it doesn’t depend on it
C. system is stable
D. system in practical equilibrium
Answer: A
Clarification: At this situation system exhibits some unwanted oscillations.

7. Is pattern storage possible if system has chaotic stability?
A. yes
B. no
Answer: A
Clarification: Pattern storage is possible if any network exhibits either fixed point, oscillatory, chaotic stability.

8. If states of system experience basins of attraction, then system may achieve what kind of stability?
A. fixed point stability
B. oscillatory stability
C. chaotic stability
D. none of the mentioned
Answer: C
Clarification: Basins of attraction is a property of chaotic stability.

9. What is an objective of a learning law?
A. to capture pattern information in training set data
B. to modify weights so as to achieve output close to desired output
C. it should lead to convergence of system or its weights
D. all of the mentioned
Answer: d
Clarification: These all are some objectives of learning laws.

10. A network will be useful only if, it leads to equilibrium state at which there is no change of state?
A. yes
B. no
Answer: A
Clarification: Its the basic condition for stability.

250+ MCQs on Hopfield Model-2 and Answers

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.

250+ MCQs on Characteristics – 2 and Answers

Neural Networks online quiz on “Characteristics – 2”.

1. What is approx size of neuron body(in micrometer)?
A. below 5
B. 5-10
C. 10-80
D. above 100
Answer: C
Clarification: Average size of neuron body lies in the above limit.

2. What is the gap at synapses(in nanometer)?
A. 50
B. 100
C. 150
D. 200
Answer: D
Clarification: It is near to 200nm.

3. What is charge at protoplasm in state of inactivity?
A. positive
B. negative
C. neutral
D. may be positive or negative
Answer: B
Clarification: It is due to the presence of potassium ion on outer surface in neural fluid.

4. What is the main constituent of neural liquid?
A. sodium
B. potassium
C. Iron
D. none of the mentioned
Answer: A
Clarification: Potassium is the main constituent of neural liquid & responsible for potential on neuron body.

5. What is average potential of neural liquid in inactive state?
A. +70mv
B. +35mv
C. -35mv
D. -70mv
Answer: D
Clarification: It is a basic fact, founded out by series of experiments conducted by neural scientist.

6. At what potential does cell membrane looses it impermeability against Na+ ions?
A. -50mv
B. -35mv
C. -60mv
D. -65mv
Answer: C
Clarification: Cell membrane looses it impermeability against Na+ ions at -60mv.

7. What is effect on neuron as a whole when its potential get raised to -60mv?
A. it get fired
B. no effect
C. it get compressed
D. it expands
Answer: A
Clarification: Cell membrane looses it impermeability against Na+ ions at -60mv.

8. The membrane which allows neural liquid to flow will?
A. never be imperturbable to neural liquid
B. regenerate & retain its original capacity
C. only the certain part get affected, while rest becomes imperturbable again
D. none of the mentioned
Answer: B
Clarification: Each cell of human body(internal) has regenerative capacity.

9. How fast is propagation of discharge signal in cells of human brain?
A. less than 0.1m/s
B. 0.5-2m/s
C. 2-5m/s
D. 5-10m/s
Answer: B
Clarification: The process is very fast but comparable to the length of neuron.

10. What is the function of neurotransmitter ?
A. they transmit data directly at synapse to other neuron
B. they modify conductance of post synaptic membrane for certain ions
C. cause polarisation or depolarisation
D. both polarisation & modify conductance of membrane
Answer: D
Clarification: Excitatory & inhibilatory activities are result of these two process.

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250+ MCQs on Neural Networks Recall and Answers

Neural Networks Multiple Choice Questions on “Recall″.

1. Lyapunov function is vector in nature?
A. yes
B. no

Answer: B
Clarification: Lyapunov function is scalar in nature.

2. What’s the role of lyaopunov fuction?
A. to determine stability
B. to determine convergence
C. both stability & convergence
D. none of the mentioned

Answer: A
Clarification: lyapunov is an energy function.

3. Did existence of lyapunov function is necessary for stability?
A. yes
B. no

Answer: B
Clarification: It is sufficient but not necessary condition.

4. V(x) is said to be lyapunov function if?
A. v(x) >=0
B. v(x) <=0
C. v(x) =0
D. none of the mentioned

Answer: B
Clarification: It is the condition for existence for lyapunov function.

5. What does cohen grossberg theorem?
A. shows the stability of fixed weight autoassociative networks
B. shows the stability of adaptive autoaassociative networks
C. shows the stability of adaptive heteroassociative networks
D. none of the mentioned

Answer: A
Clarification: Cohen grossberg theorem shows the stability of fixed weight autoassociative networks.

6. What does cohen grossberg kosko theorem?
A. shows the stability of fixed weight autoassociative networks
B. shows the stability of adaptive autoaassociative networks
C. shows the stability of adaptive heteroassociative networks
D. none of the mentioned

Answer: B
Clarification: Cohen grossberg kosko shows the stability of adaptive autoaassociative networks.

7. What does 3rd theorem that describe the stability of a set of nonlinear dynamical systems?
A. shows the stability of fixed weight autoassociative networks
B. shows the stability of adaptive autoaassociative networks
C. shows the stability of adaptive heteroassociative networks
D. none of the mentioned

Answer: C
Clarification: 3rd theorem of nonlinear dynamical systems, shows the stability of adaptive heteroassociative networks.

8. What happens during recall in neural networks?
A. weight changes are suppressed
B. input to the network determines the output activation
C. both process has to happen
D. none of the mentioned

Answer: C
Clarification: Follows from basic definition of Recall in a network.

9. Can a neural network learn & recall at the same time?
A. yes
B. no

Answer: A
Clarification: It was later proved by kosko in 1988.

10. In nearest neighbour case, the stored pattern closest to input pattern is recalled, where does it occurs?
A. feedback pattern classification
B. feedforward pattern classification
C. can be feedback or feedforward
D. none of the mentioned

Answer: B
Clarification: It is a case of feedforward networks.