# Neural Network Questions and Answers – Multi Layer Feedforward Neural Network

This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Multi Layer Feedforward Neural Network″.

1. What is the use of MLFFNN?
a) to realize structure of MLP
b) to solve pattern classification problem
c) to solve pattern mapping problem
d) to realize an approximation to a MLP

Explanation: MLFFNN stands for multilayer feedforward network and MLP stands for multilayer perceptron.

2. What is the advantage of basis function over mutilayer feedforward neural networks?
a) training of basis function is faster than MLFFNN
b) training of basis function is slower than MLFFNN
c) storing in basis function is faster than MLFFNN
d) none of the mentioned

Explanation: The main advantage of basis function is that the training of basis function is faster than MLFFNN.

3. Why is the training of basis function is faster than MLFFNN?
a) because they are developed specifically for pattern approximation
b) because they are developed specifically for pattern classification
c) because they are developed specifically for pattern approximation or classification
d) none of the mentioned

Explanation: Training of basis function is faster than MLFFNN because they are developed specifically for pattern approximation or classification.

4. Pattern recall takes more time for?
a) MLFNN
b) Basis function
c) Equal for both MLFNN and basis function
d) None of the mentioned

Explanation: The first layer of basis function involves computations.

5. In which type of networks training is completely avoided?
a) GRNN
b) PNN
c) GRNN and PNN
d) None of the mentioned

Explanation: In GRNN and PNN networks training is completely avoided.

6. What does GRNN do?
c) function approximation and pattern classification task
d) none of the mentioned

Explanation: GRNN stand for Generalized Regression Neural Networks.

7. What does PNN do?
c) function approximation and pattern classification task
d) none of the mentioned

Explanation: PNN stand for Probabilistic Neural Networks.

8. Th CPN provides practical approach for implementing?
a) patter approximation
b) pattern classification
c) pattern mapping
d) pattern clustering

Explanation: CPN i.e counterpropagation network provides a practical approach for implementing pattern mapping.

9. What consist of a basic counterpropagation network?
a) a feedforward network only
b) a feedforward network with hidden layer
c) two feedforward network with hidden layer
d) none of the mentioned

Explanation: Counterpropagation network consist of two feedforward network with a common hidden layer.

10. How does the name counterpropagation signifies its architecture?
a) its ability to learn inverse mapping functions
b) its ability to learn forward mapping functions
c) its ability to learn forward and inverse mapping functions
d) none of the mentioned

Explanation: Counterpropagation network has ability to learn forward and inverse mapping functions.

Sanfoundry Global Education & Learning Series – Neural Networks.

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