Bharathidasan University Bharathidasan University, M.C.A Degree Examination, Computer Applications, Soft Computer ,Nov 2021. model question papers



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Posted Date: 06 Feb 2022      Posted By:: Jannane.M    Member Level: Gold  Points: 3 (₹ 3)

2021 Bharathidasan University Bharathidasan University, M.C.A Degree Examination, Computer Applications, Soft Computer ,Nov 2021. Question paper



Course: M.C.A   University/board: Bharathidasan University

Are you looking for the old question papers from Bharathidasan University of Soft Computer paper? Here is the previous year question paper from Bharathidasan University. This is the original question paper of Soft Computer exam conducted in Nov 2021. Feel free to download the question paper from here and use it to prepare for your upcoming exams.



S.No. 4308

P 16 MCAE 9

(For candidates admitted from 2016–2017 onwards)

M.C.A. DEGREE EXAMINATION, NOVEMBER 2021.

Computer Applications — Elective

SOFT COMPUTING

Time : Three hours

Maximum : 75 marks

SECTION A — (10 2 = 20)

Answer ALL questions.

1.
What is Evolutionary computation?

2.
Define Inference Engine.

3.
What is meant by Linguistic variables?

4.
Define Genetic Algorithm.

5.
What is unsupervised learning?

6.
Define Adaline Back Propagation.

7.
What is Neuron functions?

8.
What is ANFIS?

9.
What are the advantages of Application of computational Intelligence?

10. What are the uses of Soft Computing?

SECTION B — (5 5 = 25)

Answer ALL questions, choosing either (a) or (b).

11. (a) What is meant by Fuzzy Reasoning? and Explain.

Or

(b) Discuss on Fuzzy modeling.

12. (a) Explain about derivative free optimization.

Or

(b) Write about Dounhill simplex search.

13. (a) Explain about organizing Networks.

Or

(b) Write in detail about Hebbian Learning.

14. (a) Explain about Coactive Neuro Fuzzy modeling.

Or

(b) Differentiate ANFIS and RBFN.

15. (a) Explain about Inverse Kinematics problems

Or

(b) Draw a neat diagram on computational Intelligence and Explain briefly.

SECTION C — (3 × 10 = 30)

Answer any THREE questions.

16. Explain about Input space partitioning and Fuzzy modeling.

17. Explain about simulated Annealing method.

18. Discuss on learning vector quantization and Give an example.

19. Explain about Neuron functions for Adaptive Networks.

20. Explain about Automobile Fuel Efficiency prediction.





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