gs gs127 Basics of Artificial Neural Networks - Learning - Quiz No.1
gs gs127 SQLite Quiz
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Question 1: On what parameters can change in weight vector depend?
learning parameters
input vector
learning signal
all of the mentioned
Question 2: If the change in weight vector is represented by ∆wij, what does it mean?
describes the change in weight vector for ith processing unit, taking input vector jth into account
describes the change in weight vector for jth processing unit, taking input vector ith into account
describes the change in weight vector for jth & ith processing unit.
none of the mentioned
Question 3: What is learning signal in this equation ∆wij= µf(wi a)aj?
µ
wi a
aj
f(wi a)
Question 4: State whether Hebb’s law is supervised learning or of unsupervised type?
supervised
unsupervised
either supervised or unsupervised
can be both supervised & unsupervised
Question 5: Hebb’s law can be represented by equation?
∆wij= µf(wi a)aj
∆wij= µ(si) aj, where (si) is output signal of ith input
both way
none of the mentioned
Question 6: State which of the following statements hold foe perceptron learning law?
it is supervised type of learning law
it requires desired output for each input
∆wij= µ(bi – si) aj
all of the mentioned
Question 8: widrow & hoff learning law is special case of?
hebb learning law
perceptron learning law
delta learning law
none of the mentioned
Question 9: What’s the other name of widrow & hoff learning law?
Hebb
LMS
MMS
None of the mentioned
Question 10: Which of the following equation represent perceptron learning law?
∆wij= µ(si) aj
∆wij= µ(bi – si) aj
∆wij= µ(bi – si) aj Á(xi),wher Á(xi) is derivative of xi
∆wij= µ(bi – (wi a)) aj
Question 11: Correlation learning law is special case of?
Hebb learning law
Perceptron learning law
Delta learning law
LMS learning law
Question 12: Correlation learning law is what type of learning?
supervised
unsupervised
either supervised or unsupervised
both supervised or unsupervised
Question 13: Correlation learning law can be represented by equation?
∆wij= µ(si) aj
∆wij= µ(bi – si) aj
∆wij= µ(bi – si) aj Á(xi),where Á(xi) is derivative of xi
∆wij= µ bi aj
Question 14: The other name for instar learning law?
looser take it all
winner take it all
winner give it all
looser give it all
Question 15: The instar learning law can be represented by equation?
∆wij= µ(si) aj
∆wij= µ(bi – si) aj
∆wij= µ(bi – si) aj Á(xi),where Á(xi) is derivative of xi
∆wk= µ (a-wk), unit k with maximum output is identified
Question 17: The instar learning law can be represented by equation?
∆wjk= µ(bj – wjk), where the kth unit is the only active in the input layer
∆wij= µ(bi – si) aj
∆wij= µ(bi – si) aj Á(xi),wher Á(xi) is derivative of xi
∆wij= µ(si) aj
Question 19: In hebbian learning intial weights are set?
random
near to zero
near to target value
near to target value
