Exam Details
| Subject | elective – i : neural networks and fuzzy control systems | |
| Paper | ||
| Exam / Course | f.y. m.tech. (civil -structural engg.) | |
| Department | ||
| Organization | solapur university | |
| Position | ||
| Exam Date | 12, December, 2018 | |
| City, State | maharashtra, solapur |
Question Paper
F.Y. M.Tech. (Electronics Engineering) (Semester (New CBCS)
Examination, 2018
Elective I neural networks and fuzzy control systems
Day and Date Wednesday, 12-12-2018 Total Marks 70
Time 10.00 a.m. to 1.00 p.m.
Instructions All questions are compulsory.
Figures to the right indicate full marks.
Assume suitable data if necessary.
SECTION I
1. What are limitations of standard error back propagation How they can
be overcome 7
Explain gradient type Hopfield ANN. 6
2. Solve any two.
Explain any one application of ANN. 6
Explain simulated annealing based ANN. 6
Explain ARMA model. 6
3. Solve any two.
Explain independent component analysis. 5
Explain linear system identification. 5
Differentiate between supervised and unsupervised learning in ANN. 5
Seat
No. Set P
P.T.O.
SECTION II
4. With suitable diagram explain typical fuzzy controller. 7
Explain PID like fuzzy controller. 6
5. Solve any two.
Explain non linear simulation using fuzzy rule based systems. 6
With suitable explain any one application of fuzzy control system
in detail. 6
Comment on knowledge representation in fuzzy systems. 6
6. Solve any two.
Differentiate between regular set theory and fuzzy set theory. 5
Explain any one method of defuzzyfication. 5
Explain fuzzy controller from industrial perspective. 5
Examination, 2018
Elective I neural networks and fuzzy control systems
Day and Date Wednesday, 12-12-2018 Total Marks 70
Time 10.00 a.m. to 1.00 p.m.
Instructions All questions are compulsory.
Figures to the right indicate full marks.
Assume suitable data if necessary.
SECTION I
1. What are limitations of standard error back propagation How they can
be overcome 7
Explain gradient type Hopfield ANN. 6
2. Solve any two.
Explain any one application of ANN. 6
Explain simulated annealing based ANN. 6
Explain ARMA model. 6
3. Solve any two.
Explain independent component analysis. 5
Explain linear system identification. 5
Differentiate between supervised and unsupervised learning in ANN. 5
Seat
No. Set P
P.T.O.
SECTION II
4. With suitable diagram explain typical fuzzy controller. 7
Explain PID like fuzzy controller. 6
5. Solve any two.
Explain non linear simulation using fuzzy rule based systems. 6
With suitable explain any one application of fuzzy control system
in detail. 6
Comment on knowledge representation in fuzzy systems. 6
6. Solve any two.
Differentiate between regular set theory and fuzzy set theory. 5
Explain any one method of defuzzyfication. 5
Explain fuzzy controller from industrial perspective. 5
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