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This section includes 12 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Networks knowledge and support exam preparation. Choose a topic below to get started.
1. |
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 |
Answer» D. none of the mentioned | |
2. |
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 |
Answer» D. none of the mentioned | |
3. |
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 |
Answer» D. none of the mentioned | |
4. |
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 |
Answer» D. none of the mentioned | |
5. |
Th CPN provides practical approach for implementing? |
A. | patter approximation |
B. | pattern classification |
C. | pattern mapping |
D. | pattern clustering |
Answer» D. pattern clustering | |
6. |
What does PNN do? |
A. | function approximation task |
B. | pattern classification task |
C. | function approximation and pattern classification task |
D. | none of the mentioned |
Answer» C. function approximation and pattern classification task | |
7. |
What does GRNN do? |
A. | function approximation task |
B. | pattern classification task |
C. | function approximation and pattern classification task |
D. | none of the mentioned |
Answer» B. pattern classification task | |
8. |
In which type of networks training is completely avoided? |
A. | GRNN |
B. | PNN |
C. | GRNN and PNN |
D. | None of the mentioned |
Answer» D. None of the mentioned | |
9. |
Pattern recall takes more time for? |
A. | MLFNN |
B. | Basis function |
C. | Equal for both MLFNN and basis function |
D. | None of the mentioned |
Answer» C. Equal for both MLFNN and basis function | |
10. |
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 |
Answer» D. none of the mentioned | |
11. |
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 |
Answer» B. training of basis function is slower than MLFFNN | |
12. |
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 |
Answer» E. | |