Soft Computing FT1
  • 1. ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems.
A) FALSE
B) TRUE
  • 2. Artificial neural network used for
A) All of these
B) Clustering
C) Classification
D) Pattern recognition
  • 3. A Neural Network can answer
A) IF-The-Else Analysis Questions
B) What-if question
C) For Loop questions
  • 4. Ability to learn how to do tasks based on the data given for training or initial experience
A) Fault tolerance
B) Robustness
C) Self Organization
D) Adaptive Learning
  • 5. Feature of ANN in which ANN  creates its own organization or representation of information it receives during learning time is
A) Self Organization
B) What-If Analysis
C) Supervised Learning
D) Adaptive Learning
  • 6. In artificial Neural Network interconnected processing elements are called
A) nodes or neurons
B) weights
C) Soma
D) axons
  • 7. Each connection link in ANN is associated with ________  which has information about the input signal.
A) activation function
B) weights
C) bias
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) Bias
D) activation or activity level of neuron
  • 10. Neuron can send  ________  signal at a time.
A) none
B) one
C) multiple
D) any number of
  • 11. The network that involves backward links from output to the input and hidden layers is called as ____.
A) Perceptrons
B) Self organizing maps
C) Recurrent neural network
D) Multi layered perceptron
  • 12. Automated vehicle is an example of ______.
A) Unsupervised learning
B) Supervised learning
C) Active learning
D) Reinforcement learning
  • 13. In an Unsupervised learning.
A) Both inputs and outputs are given
B) specific output values are not given
C) Specific output values are given
D) No specific Inputs are 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 perceive everything as a pattern while machine perceive it merely as data
B) human have sense organs
C) human have more IQ & intellect
D) human have emotions
  • 19. The fundamental unit of network is
A) neuron
B) nucleus
C) axon
D) brain
  • 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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