Exam Details
| Subject | elective iii — soft computing | |
| Paper | ||
| Exam / Course | m.sc. computer science | |
| Department | ||
| Organization | alagappa university | |
| Position | ||
| Exam Date | November, 2017 | |
| City, State | tamil nadu, karaikudi |
Question Paper
M.Sc. DEGREE EXAMINATION, NOVEMBER 2017
Third Semester
Computer Science
Elective III — SOFT COMPUTING
(CBCS 2016 onwards)
Time 3 Hours Maximum 75 Marks
Part A (10 x 2 20)
Answer all questions.
All questions carry equal marks.
1. List out the components of soft computing.
2. Define: Activation Function.
3. List out the applications of back propagation network.
4. What is Associative Memory?
5. Define: Crisp Set.
6. Define: Membership function.
7. What are Quantifiers?
8. List out the applications of Fuzzy Logic.
9. Define Mutation.
10. What is Fitness Function?
Sub. Code
551560
RW-985
2
Wk 4
Part B x 5 25)
Answer all questions choosing either or
All questions carry equal marks.
11. Explain the characteristics of soft computing.
Or
Discuss Recurrent Neural Networks.
12. Explain the Bidirectional Associative Memory.
Or
Discuss the effects of tuning parameters of the back
propagation neural networks.
13. Explain the properties of fuzzy sets.
Or
Explain the operations of fuzzy relations.
14. Explain the fuzzy inference systems.
Or
Discuss the laws of prepositional logic.
15. Discuss the Schema theorem.
Or
Explain multimodal optimization in genetic
algorithm.
Part C x 10 30)
Answer any three questions.
All questions carry equal marks.
16. Describe the learning methods.
17. Explain the architecture of back propagation network.
RW-985
3
Wk 4
18. Discuss the operations of fuzzy set.
19. Explain the Defuzzification methods.
20. Discuss about optimization of travelling sales man
problem using genetic algorithm.
———————
Third Semester
Computer Science
Elective III — SOFT COMPUTING
(CBCS 2016 onwards)
Time 3 Hours Maximum 75 Marks
Part A (10 x 2 20)
Answer all questions.
All questions carry equal marks.
1. List out the components of soft computing.
2. Define: Activation Function.
3. List out the applications of back propagation network.
4. What is Associative Memory?
5. Define: Crisp Set.
6. Define: Membership function.
7. What are Quantifiers?
8. List out the applications of Fuzzy Logic.
9. Define Mutation.
10. What is Fitness Function?
Sub. Code
551560
RW-985
2
Wk 4
Part B x 5 25)
Answer all questions choosing either or
All questions carry equal marks.
11. Explain the characteristics of soft computing.
Or
Discuss Recurrent Neural Networks.
12. Explain the Bidirectional Associative Memory.
Or
Discuss the effects of tuning parameters of the back
propagation neural networks.
13. Explain the properties of fuzzy sets.
Or
Explain the operations of fuzzy relations.
14. Explain the fuzzy inference systems.
Or
Discuss the laws of prepositional logic.
15. Discuss the Schema theorem.
Or
Explain multimodal optimization in genetic
algorithm.
Part C x 10 30)
Answer any three questions.
All questions carry equal marks.
16. Describe the learning methods.
17. Explain the architecture of back propagation network.
RW-985
3
Wk 4
18. Discuss the operations of fuzzy set.
19. Explain the Defuzzification methods.
20. Discuss about optimization of travelling sales man
problem using genetic algorithm.
———————
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