1. In what ways can output be determined from activation value?
a) deterministically
b) stochastically
c) both deterministically & stochastically
d) none of the mentioned
Explanation: This is the most important trait of input processing & output determination in neural networks.
2. How can output be updated in neural network?
a) synchronously
b) asynchronously
c) both synchronously & asynchronously
d) none of the mentioned
Explanation: Output can be updated at same time or at different time in the networks.
3. What is asynchronous update in neural netwks?
a) output units are updated sequentially
b) output units are updated in parallel fashion
c) can be either sequentially or in parallel fashion
d) none of the mentioned
Explanation: Output are updated at different time in the networks.
4. Who invented perceptron neural networks?
a) McCullocch-pitts
b) Widrow
c) Minsky & papert
d) Rosenblatt
Explanation: 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
5. What was the 2nd stage in perceptron model called?
a) sensory units
b) summing unit
c) association unit
d) output unit
Explanation: This was the very speciality of the perceptron model, that is performs association mapping on outputs of he sensory units.
6. 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
Explanation: The weights in perceprton model are adjustable.
7. 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
Explanation: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.
8. 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
Explanation: The correct answer is n^a(i).
9. What is adaline in neural networks?
a) adaptive linear element
b) automatic linear element
c) adaptive line element
d) none of the mentioned
Explanation: adaptive linear element is the full form of adaline neural model.
10. who invented the adaline neural model?
a) Rosenblatt
b) Hopfield
c) Werbos
d) Widrow
Explanation: Widrow invented the adaline neural model.