Neural Networks Multiple Choice Questions on “Competitive Learning Neural Nework Introduction″.
1. How are input layer units connected to second layer in competitive learning networks?
A. feedforward manner
B. feedback manner
C. feedforward and feedback
D. feedforward or feedback
Answer: A
Clarification: The output of input layer is given to second layer with adaptive feedforward weights.
2. Which layer has feedback weights in competitive neural networks?
A. input layer
B. second layer
C. both input and second layer
D. none of the mentioned
Answer: B
Clarification: Second layer has weights which gives feedback to the layer itself.
3. What is the nature of general feedback given in competitive neural networks?
A. self excitatory
B. self inhibitory
C. self excitatory or self inhibitory
D. none of the mentioned
Answer: A
Clarification: The output of each unit in second layer is fed back to itself in self – excitatory manner.
4. What consist of competitive learning neural networks?
A. feedforward paths
B. feedback paths
C. either feedforward or feedback
D. combination of feedforward and feedback
Answer: Competitive learning neural networks is a combination of feedforward and feedback connection layers resulting in some kind of competition.
5. What conditions are must for competitive network to perform pattern clustering?
A. non linear output layers
B. connection to neighbours is excitatory and to the farther units inhibitory
C. on centre off surround connections
D. none of the mentioned fulfils the whole criteria
Answer: D
Clarification: If the output functions of units in feedback laye are made non-linear , with fixed weight on-centre off-surround connections, the pattern clustering can be performed.
6. What conditions are must for competitive network to perform feature mapping?
A. non linear output layers
B. connection to neighbours is excitatory and to the farther units inhibitory
C. on centre off surround connections
D. none of the mentioned fulfils the whole criteria
Answer: D
Clarification: If cndition in a, b, c are met then feature mapping can be performed.
7. If a competitive network can perform feature mapping then what is that network can be called?
A. self excitatory
B. self inhibitory
C. self organization
D. none of the mentioned
Answer: C
Clarification: Competitive network that can perform feature mapping can be called as self organization network.
8. What is an instar?
A. receives inputs from all others
B. gives output to all others
C. may receive or give input or output to others
D. none of the mentioned
Answer: A
Clarification: An instar receives inputs from all other input units.
9. How is weight vector adjusted in basic competitive learning?
A. such that it moves towards the input vector
B. such that it moves away from input vector
A. such that it moves towards the output vector
B. such that it moves away from output vector
Answer: A
Clarification: Weight vector is adjusted such that it moves towards the input vector.
10. The update in weight vector in basic competitive learning can be represented by?
A. w(t + 1) = w(t) + del.w(t)
B. w(t + 1) = w(t)
C. w(t + 1) = w(t) – del.w(t)
D. none of the mentioned
Answer: A
Clarification: The update in weight vector in basic competitive learning can be represented by w(t + 1) = w(t) + del.w(t).