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