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This quiz contains multiple-choice problems on associative networks, neural network applications and the concepts of feedforward neural networks.
What tasks cannot be realised or recognised by simple networks?
Handwritten characters
Speech sequences
Image sequences
All of the above
What does a network become if the weight matrix stores multiple associations among several patterns?
Auto-assoiative memory
Heteroassociative memory
Multi-directional associative memory
Temporal associative memory
What does a network become if the weight matrix stores the given patterns?
Auto-assoiative memory
Heteroassociative memory
Multi-directional associative memory
Temporal associative memory
What does a network become if the weight matrix stores an association between a pair of patterns?
Auto-assoiative memory
Heteroassociative memory
Multi-directional associative memory
Temporal associative memory
What does the network become if the weight matrix stores an association between adjacent pairs of patterns?
Auto-associative memory
Heteroassociative memory
Multi-directional associative memory
Temporal associative memory
What is heteroassociative memory's other name?
Uni-directional memory
Bi-directional memory
Multi-directional associative memory
Temporal associative memory
What are some desirable characteristics of associative memories?
Ability to store large number of patterns
Fault tolerance
Able to recall, even for input pattern is noisy
All of the above
What is the objective of BAM?
To store pattern pairs
To recall pattern pairs
To store a set of pattern pairs which can be recalled by giving either pattern as input
None of the above
Is BAM a special case of MAM?
Yes
No
What is the use of MLFFNN?
To realize the structure of MLP
To solve pattern classification problems
To solve pattern mapping problems
To realize an approximation to a MLP
What is the advantage of basis function over mutilayer feedforward neural networks?
Training of basis function is faster than MLFFNN
Training of basis function is slower than MLFFNN
Storing in basis function is faster than MLFFNN
None of the above
Pattern recall takes more time for
MLFNN
Basis function
Equal for both MLFNN and basis function
None of the above
Why is basis function training faster than MLFFNN?
Because they are developed specifically for pattern approximation
Because they are developed specifically for pattern classification
Because they are developed specifically for pattern approximation or classification
None of the above
For what type of networks is training avoided completely?
GRNN
PNN
GRNN and PNN
None of the above
Can data be stored directly in associative memory?
Yes
No
