gs gs127 Competitive Learning Neural Networks - Competitive Learning Neural Network Introduction - Quiz No.1
gs gs127 SQLite Quiz
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Question 1: How are input layer units connected to second layer in competitive learning networks?
feedforward manner
feedback manner
feedforward and feedback
feedforward or feedback
Question 2: Which layer has feedback weights in competitive neural networks?
input layer
second layer
both input and second layer
none of the mentioned
Question 3: What is the nature of general feedback given in competitive neural networks?
self excitatory
self inhibitory
self excitatory or self inhibitory
none of the mentioned
Question 4: What consist of competitive learning neural networks?
feedforward paths
feedback paths
either feedforward or feedback
combination of feedforward and feedback
Question 5: What conditions are must for competitive network to perform pattern clustering?
non linear output layers
connection to neighbours is excitatory and to the farther units inhibitory
on centre off surround connections
none of the mentioned fulfils the whole criteria
Question 6: What conditions are must for competitive network to perform feature mapping?
non linear output layers
connection to neighbours is excitatory and to the farther units inhibitory
on centre off surround connections
none of the mentioned fulfils the whole criteria
Question 7: If a competitive network can perform feature mapping then what is that network can be called?
self excitatory
self inhibitory
self organization
none of the mentioned
Question 8: What is an instar?
receives inputs from all others
gives output to all others
may receive or give input or output to others
none of the mentioned
Question 9: How is weight vector adjusted in basic competitive learning?
such that it moves towards the input vector
such that it moves away from input vector
such that it moves towards the output vector
such that it moves away from output vector
Question 10: The update in weight vector in basic competitive learning can be represented by?
w(t + 1) = w(t) + del.w(t)
w(t + 1) = w(t)
w(t + 1) = w(t) – del.w(t)
none of the mentioned
