Download PTU M.Tech. ECE 2nd Semester 76263 NEURAL NETWORKS Question Paper

Download PTU. I.K. Gujral Punjab Technical University (IKGPTU) M.Tech. ECE 2nd Semester 76263 NEURAL NETWORKS Question Paper.

1 | M-76263 (S35)-2804

Roll No. Total No. of Pages : 02
Total No. of Questions : 8
M.Tech. (ECE) (2018 Batch) (Sem.?2)
NEURAL NETWORKS
Subject Code : MTEC-PE3C-18
M.Code : 76263
Time : 3 Hrs. Max. Marks : 60

INSTRUCTIONS TO CANDIDATES :
1.Attempt any FIVE questions out of EIGHT questions.
2.Each question carries TWELVE marks.


Q1 a) Write the types of various activation functions used in neural network. Explain two of
them in detail. [06]
b) What is learning? Explain the reinforcement learning with an example. [06]
Q2 a) Discuss the importance of back propagation algorithm. Explain one of the
applications in detail. [06]
b) Draw and explain the working of McCulloch-Pitts model of neural network. [06]
Q3 a) What is content addressable memory? Discuss the difference between instar and
outstar learning rule. [06]
b) Why are symmetrical weights and weights with no self connection important in
discrete Hopfield network? [06]
Q4 a) What is perceptron? Explain perceptron learning rule algorithm in detail. [06]
b) Define Hamming distance. Explain the usefulness of Hamming distance in any
artificial neural network based application with an example [06]
Q5 a) Differentiate a fuzzy set and crisp set based on their properties with an example.
[06]
b) Define Fuzzification. Explain any one of the fuzzification scheme with an example.
[06]

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1 | M-76263 (S35)-2804

Roll No. Total No. of Pages : 02
Total No. of Questions : 8
M.Tech. (ECE) (2018 Batch) (Sem.?2)
NEURAL NETWORKS
Subject Code : MTEC-PE3C-18
M.Code : 76263
Time : 3 Hrs. Max. Marks : 60

INSTRUCTIONS TO CANDIDATES :
1.Attempt any FIVE questions out of EIGHT questions.
2.Each question carries TWELVE marks.


Q1 a) Write the types of various activation functions used in neural network. Explain two of
them in detail. [06]
b) What is learning? Explain the reinforcement learning with an example. [06]
Q2 a) Discuss the importance of back propagation algorithm. Explain one of the
applications in detail. [06]
b) Draw and explain the working of McCulloch-Pitts model of neural network. [06]
Q3 a) What is content addressable memory? Discuss the difference between instar and
outstar learning rule. [06]
b) Why are symmetrical weights and weights with no self connection important in
discrete Hopfield network? [06]
Q4 a) What is perceptron? Explain perceptron learning rule algorithm in detail. [06]
b) Define Hamming distance. Explain the usefulness of Hamming distance in any
artificial neural network based application with an example [06]
Q5 a) Differentiate a fuzzy set and crisp set based on their properties with an example.
[06]
b) Define Fuzzification. Explain any one of the fuzzification scheme with an example.
[06]

2 | M-76263 (S35)-2804

Q6 a) State the basic principle of Sugeno inference technique with an example. [06]
b) Explain the significance of fuzzy t-norm and t-conorm operators with an example.
[06]
Q7 a) Explain the concept of fuzzy uncertainty and fuzzy logic with suitable example. [06]
b) Draw and explain fuzzy-neuro system with neat block diagram and suitable example.
[06]
Q8 Draw and explain the various steps required to implement genetic algorithm with the help
of block diagram. Discuss each of them in detail. [12]















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