Neural Networks Question and Answers – Pattern Mapping

This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Pattern Mapping″.

1. Can all hard problems be handled by a multilayer feedforward neural network, with nonlinear units?
a) yes
b) no
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2. What is a mapping problem?
a) when no restrictions such as linear separability is placed on the set of input – output pattern pairs
b) when there may be restrictions such as linear separability placed on input – output patterns
c) when there are restriction but other than linear separability
d) none of the mentioned
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3. Can mapping problem be a more general case of pattern classification problem?
a) yes
b) no
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4. What is the objective of pattern mapping problem?
a) to capture weights for a link
b) to capture inputs
c) to capture feedbacks
d) to capture implied function
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5. To provide generalization capability to a network, what should be done?
a) all units should be linear
b) all units should be non – linear
c) except input layer, all units in other layers should be non – linear
d) none of the mentioned
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6. What is the objective of pattern mapping problem?
a) to capture implied function
b) to capture system characteristics from observed data
c) both to implied function and system characteristics
d) none of the mentioned
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7. Does an approximate system produce strictly an interpolated output?
a) yes
b) no
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8. The nature of mapping problem decides?
a) number of units in second layer
b) number of units in third layer
c) overall number of units in hidden layers
d) none of the mentioned
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9. How is hard learning problem solved?
a) using nonlinear differentiable output function for output layers
b) using nonlinear differentiable output function for hidden layers
c) using nonlinear differentiable output function for output and hidden layers
d) it cannot be solved
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10. The number of units in hidden layers depends on?
a) the number of inputs
b) the number of outputs
c) both the number of inputs and outputs
d) the overall characteristics of the mapping problem
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Sanfoundry Global Education & Learning Series – Neural Networks.

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