Neural Networks

Introduction of Feedback Neural Network

Question 1
Marks : +2 | -2
Pass Ratio : 14%
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is accretive in nature?
a(l)
a(l) + e
could be either a(l) or a(l) + e
e
Explanation:
This is the property of accretive system.
Question 2
Marks : +2 | -2
Pass Ratio : 14%
What property should a feedback network have, to make it useful for storing information?
accretive behaviour
interpolative behaviour
both accretive and interpolative behaviour
none of the mentioned
Explanation:
During recall accretive behaviour make it possible for system to store information.
Question 3
Marks : +2 | -2
Pass Ratio : 14%
Linear neurons can be useful for application such as interpolation, is it true?
yes
no
Explanation:
This means for input vector x, output vector y is produced and for input a.x, output will be a.y.
Question 4
Marks : +2 | -2
Pass Ratio : 14%
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network?
a(l)
a(l) + e
could be either a(l) or a(l) + e
e
Explanation:
This is due to the absence of accretive behaviour.
Question 5
Marks : +2 | -2
Pass Ratio : 29%
What is the objective of a pattern storage task in a network?
to store a given set of patterns
to recall a give set of patterns
both to store and recall
none of the mentioned
Explanation:
The objective of a pattern storage task in a network is to store and recall a given set of patterns.
Question 6
Marks : +2 | -2
Pass Ratio : 14%
Is there any error in linear autoassociative networks?
yes
no
Explanation:
Because input comes out as output.
Question 7
Marks : +2 | -2
Pass Ratio : 14%
What is a Boltzman machine?
A feedback network with hidden units
A feedback network with hidden units and probabilistic update
A feed forward network with hidden units
A feed forward network with hidden units and probabilistic update
Explanation:
Boltzman machine is a feedback network with hidden units and probabilistic update.
Question 8
Marks : +2 | -2
Pass Ratio : 14%
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature?
a(l)
a(l) + e
could be either a(l) or a(l) + e
e
Explanation:
This is the property of interpolative system.
Question 9
Marks : +2 | -2
Pass Ratio : 14%
How can false minima be reduced in case of error in recall in feedback neural networks?
by providing additional units
by using probabilistic update
can be either probabilistic update or using additional units
none of the mentioned
Explanation:
Hard problem can be solved by additional units not the false minima.
Question 10
Marks : +2 | -2
Pass Ratio : 14%
What is objective of linear autoassociative feedforward networks?
to associate a given pattern with itself
to associate a given pattern with others
to associate output with input
none of the mentioned
Explanation:
The objective of linear autoassociative feedforward networks is to associate a given pattern with itself.