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1. Who invented perceptron neural networks?
a) McCullocch-pitts
b) Widrow
c) Minsky & papert
d) Rosenblatt

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Answer: d [Reason:] The perceptron is one of the earliest neural networks. Invented at the Cornell Aeronautical Laboratory in 1957 by Frank Rosenblatt, the Perceptron was an attempt to understand human memory, learning, and cognitive processes.

2. What was the 2nd stage in perceptron model called?
a) sensory units
b) summing unit
c) association unit
d) output unit

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Answer: c [Reason:] This was the very speciality of the perceptron model, that is performs association mapping on outputs of he sensory units.

3. What was the main deviation in perceptron model from that of MP model?
a) more inputs can be incorporated
b) learning enabled
c) all of the mentioned
d) none of the mentioned

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Answer: b [Reason:] The weights in perceprton model are adjustable.

4. What is delta (error) in perceptron model of neuron?
a) error due to environmental condition
b) difference between desired & target output
c) can be both due to difference in target output or environmental condition
d) none of the mentioned

View Answer

Answer: a [Reason:] All other parameters are assumed to be null while calculatin the error in perceptron model & only difference between desired & target output is taken into account.

5. If a(i) is the input, ^ is the error, n is the learning parameter, then how can weight change in a perceptron model be represented?
a) na(i)
b) n^
c) ^a(i)
d) none of the mentioned

View Answer

Answer: d [Reason:] The correct answer is n^a(i).

6. What is adaline in neural networks?
a) adaptive linear element
b) automatic linear element
c) adaptive line element
d) none of the mentioned

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Answer: a [Reason:] adaptive linear element is the full form of adaline neural model.

7. who invented the adaline neural model?
a) Rosenblatt
b) Hopfield
c) Werbos
d) Widrow

View Answer

Answer: d [Reason:] Widrow invented the adaline neural model.

8. What was the main point of difference between the adaline & perceptron model?
a) weights are compared with output
b) sensory units result is compared with output
c) analog activation value is compared with output
d) all of the mentioned

View Answer

Answer: c [Reason:] Analog activation value comparison with output,instead of desired output as in perceptron model was the main point of difference between the adaline & perceptron model.

9. In adaline model what is the relation between output & activation value(x)?
a) linear
b) nonlinear
c) can be either linear or non-linear
d) none of the mentioned

View Answer

Answer: a [Reason:] s,output=f(x)=x. Hence its a linear model.

10. what is the another name of weight update rule in adaline model based on its functionality?
a) LMS error learning law
b) gradient descent algorithm
c) both LMS error & gradient descent learning law
d) none of the mentioned

View Answer

Answer: c [Reason:] weight update rule minimizes the mean squared error(delta square), averaged over all inputs & this laws is derived using negative gradient of error surface weight space, hence option a & b.

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