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Soft Computing FT1
Contributed by: V
  • 1. ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems.
A) TRUE
B) FALSE
  • 2. Artificial neural network used for
A) All of these
B) Classification
C) Pattern recognition
D) Clustering
  • 3. A Neural Network can answer
A) What-if question
B) For Loop questions
C) IF-The-Else Analysis Questions
  • 4. Ability to learn how to do tasks based on the data given for training or initial experience
A) Self Organization
B) Adaptive Learning
C) Robustness
D) Fault tolerance
  • 5. Feature of ANN in which ANN  creates its own organization or representation of information it receives during learning time is
A) Adaptive Learning
B) What-If Analysis
C) Self Organization
D) Supervised Learning
  • 6. In artificial Neural Network interconnected processing elements are called
A) Soma
B) nodes or neurons
C) weights
D) axons
  • 7. Each connection link in ANN is associated with ________  which has information about the input signal.
A) bias
B) activation function
C) weights
D) neurons
  • 8. Neurons or artificial neurons  have the capability to model networks of original neurons as found in brain
A) TRUE
B) FALSE
  • 9. Internal state of neuron is called __________,  is the function of the inputs the neurons receives
A) Weight
B) None of these
C) activation or activity level of neuron
D) Bias
  • 10. Neuron can send  ________  signal at a time.
A) one
B) any number of
C) none
D) multiple
  • 11. The network that involves backward links from output to the input and hidden layers is called as ____.
A) Self organizing maps
B) Recurrent neural network
C) Perceptrons
D) Multi layered perceptron
  • 12. Automated vehicle is an example of ______.
A) Supervised learning
B) Unsupervised learning
C) Active learning
D) Reinforcement learning
  • 13. In an Unsupervised learning.
A) No specific Inputs are given
B) Both inputs and outputs are given
C) Specific output values are given
D) specific output values are not given
  • 14. Neural Networks are complex -----------------------with many parameters.
A) Linear Functions
B) Exponential Functions
C) Discrete Functions
D) Nonlinear Functions
  • 15. Neural networks in which information is fed both backward and forward are called as ________
A) Feedforward neural networks
B) Recurrent neural networks
  • 16. Neural networks in which output from one layer is fed as input to another layer are called as ________
A) Feedforward neural networks
B) Recurrent neural networks
  • 17. Activation models are --------------
A) Static
B) Deterministic
C) Dynamic
  • 18. What’s the main point of difference between human & machine intelligence?
A) human have more IQ & intellect
B) human perceive everything as a pattern while machine perceive it merely as data
C) human have emotions
D) human have sense organs
  • 19. The fundamental unit of network is
A) neuron
B) axon
C) brain
D) nucleus
  • 20. What is hebb’s rule of learning
A) the system recalls previous reference inputs & respective ideal outputs
B) the system learns from its past mistakes
C) the strength of neural connection get modified accordingly
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