Artificial Neural Networks Mid - I, September - 2014

1.Neurons are connected in__________
  • Series
  • Parallel
  • A complex
  • Individual
Answer: B
2.Axons are____________
  • Parts of the cell body
  • Connections between dendrites
  • Neuron outputs
  • Neuron inputs
Answer: C
3.Supervised learning is ____________
  • Always possible
  • Not always possible
  • Partial d) Both
Answer: B
4.Perceptions are suitable for
  • Single layer only
  • Multi-layer only
  • Single and multi layer
  • Single neuron only
Answer: B
5.What is back propagation?
  • It is another name given to the curvy function in the perceptron
  • It is the transmission of error back through the network to adjust the inputs
  • It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
  • None of the mentioned
Answer: C
6.Neural Networks are complex ______________ with many parameters.
  • Linear Functions
  • Nonlinear Functions
  • Discrete Functions
  • Exponential Functions
Answer: A
7.Machine Learning Involves
  • Learning from the successful move
  • copying knowledge from humans to computers
  • loading numerous games and playing regularly
  • making numerous mistakes so computer can learn
Answer: A
8.Activation functions play an important role in many ANN's
  • Networking
  • Communication
  • Neural Network
  • Internet connection
Answer: C
9.A multilayer perceptron (MLP) is a_________
  • Feed Back
  • Multi layered
  • Single Layered
  • Feed forward
Answer: D
10.Stochastic neural networks is a_________
  • Back Propagation Algorithm
  • Security Algorithm
  • NN Algorithm
  • Feed forward Algorithm
Answer: A
11.A perception is a___________________.
Answer: A single layer feed-forward neural network
12.Self-Organizing Feature Maps are principally _______________learning.
Answer: Unsupervised
13.In competitive learning when a training example is fed to the network, its Euclidean distance to all weight vectors is ______________.
Answer: Computed
14.SOM may be considered a nonlinear generalization of_______________.
Answer: Principal components analysis (PCA)
15.Non-associative learning can be divided into______________ and______________.
Answer: Habituation & Sensitization
16.Sensitization is an example of _________________.
Answer: Non-associative learning
17.A multilayer perceptron (MLP) is a________________ network.
Answer: Feed forward
18.Multilayer perceptron has a linear __________________funtion in all neurons.
Answer: Activation
19.Back propagation is a________________ learning.
Answer: Supervised Learning
20.Three-layered multilayer neural network with two-layer(middle) is_______________ neurons.
Answer: Hidden