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OR

6. Let (0), (0.8), (0.8), (1)A a b c d

(0.2), (1), (0.8), (0)B a b c d

(0), (0.4), (1), (0.8)C a b c d

Compute

( ) ( )a A B C 6

(b) ( ) ( )A B A C 6

UNIT-IV

7. Explain Fuzzification and Defuzzification Methods in detail 12

OR

8. Write down the steps involved in Fuzzy-base rule based control

systems 12

UNIT-V

9. Explain Fuzzy base Home heating system with the neat diagram 12

OR

10. Write short notes on:a) Adaptive fuzzy systems 6

b) Hybrid systems 6

[08/II S/212]

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[April-12]

[EPRPA 204A]

M.Tech. Degree Examination

Power Systems & AutomationII SEMESTER

ARTIFICIAL NEURAL NETWORKS & FUZZY SYSTEMS(Effective from the admitted batch 201011)

Time: 3 Hours Max.Marks: 60

----------------------------------------------------------------------------------------------------

Instructions: Each Unit carries 12 marks.

Answer all units choosing one question from each unit.

All parts of the unit must be answered in one place only.

Figures in the right hand margin indicate marks allotted.

----------------------------------------------------------------------------------------------------

UNIT-I

1. Draw and explain in detail about the various Neural Network

Architectures 12OR

2. Explain with the diagram how the Biological Neural Network is

inspired to formulate ANN. What are the properties of ANN 12

UNIT-II

3. Explain Least Mean Square algorithm. How it is used in Back

Propagation Neural Network 12OR

4. Distinguish supervised and unsupervised learning algorithms with

the examples 12

UNIT-III

5. a) Explain the properties of Fuzzy sets with examples 6

b) Explain the methods of converting crisp to Fuzzy value and

vice versa with the example 6

8/12/2019 EPRPA 204A

3/4

OR

6. Let (0), (0.8), (0.8), (1)A a b c d

(0.2), (1), (0.8), (0)B a b c d

(0), (0.4), (1), (0.8)C a b c d

Compute

( ) ( )a A B C 6

(b) ( ) ( )A B A C 6

UNIT-IV

7. Explain Fuzzification and Defuzzification Methods in detail 12

OR

8. Write down the steps involved in Fuzzy-base rule based control

systems 12

UNIT-V

9. Explain Fuzzy base Home heating system with the neat diagram 12

OR

10. Write short notes on:a) Adaptive fuzzy systems 6

b) Hybrid systems 6

[08/II S/212]

8/12/2019 EPRPA 204A

4/4

[April-12]

[EPRPA 204A]

M.Tech. Degree Examination

Power Systems & AutomationII SEMESTER

ARTIFICIAL NEURAL NETWORKS & FUZZY SYSTEMS(Effective from the admitted batch 201011)

Time: 3 Hours Max.Marks: 60

----------------------------------------------------------------------------------------------------

Instructions: Each Unit carries 12 marks.

Answer all units choosing one question from each unit.

All parts of the unit must be answered in one place only.

Figures in the right hand margin indicate marks allotted.

----------------------------------------------------------------------------------------------------

UNIT-I

1. Draw and explain in detail about the various Neural Network

Architectures 12OR

2. Explain with the diagram how the Biological Neural Network is

inspired to formulate ANN. What are the properties of ANN 12

UNIT-II

3. Explain Least Mean Square algorithm. How it is used in Back

Propagation Neural Network 12OR

4. Distinguish supervised and unsupervised learning algorithms with

the examples 12

UNIT-III

5. a) Explain the properties of Fuzzy sets with examples 6

b) Explain the methods of converting crisp to Fuzzy value and

vice versa with the example 6