Neural Networks Questions and Answers - Terminology

1. Who proposed the first perceptron model in 1958?
a) McCulloch-pitts
b) Marvin Minsky
c) Hopfield
d) Rosenblatt

Answer: d
Explanation: Rosenblatt proposed the first perceptron model in 1958 .

2. John hopfield was credited for what important aspec of neuron?
a) learning algorithms
b) adaptive signal processing
c) energy analysis
d) none of the mentioned

Answer: c
Explanation: It was of major contribution of his works in 1982.

3. What is the contribution of Ackley, Hinton in neural?
a) perceptron
b) boltzman machine
c) learning algorithms
d) none of the mentioned

Answer: b
Explanation: Ackley, Hinton built the boltzman machine

4. What is ART in neural networks?
a) automatic resonance theory
b) artificial resonance theory
c) adaptive resonance theory
d) none of the mentioned

Answer: c
Explanation: It is full form of ART & is basic q&a

5. What is an activation value?
a) weighted sum of inputs
b) threshold value
c) main input to neuron
d) none of the mentioned

Answer: a
Explanation:It is definition of activation value & is basic q&a.

6. Positive sign of weight indicates?
a) excitatory input
b) inhibitory input
c) can be either excitatory or inhibitory as such
d) none of the mentioned

Answer: a
Explanation: Sign convention of neuron.

7. Negative sign of weight indicates?
a) excitatory input
b) inhibitory input
c) excitatory output
d) inhibitory output

Answer: b
Explanation: Sign convention of neuron.

8. The amount of output of one unit received by another unit depends on what?
a) output unit
b) input unit
c) activation value
d) weight

Answer: d
Explanation: Activation is sum of wieghted sum of inputs, which gives desired output. Hence output depends on weights

9. The process of adjusting the weight is known as?
a) activation
b) synchronisation
c) learning
d) none of the mentioned

Answer: c
Explanation: Basic definition of learning in neural nets .

10. The procedure to incrementally update each of weights in neural is referred to as?
a) synchronisation
b) learning law
c) learning algorithm
d) both learning algorithm & law

Answer: d
Explanation: Basic definition of learning law in neural.