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Total No. of Pages : 02
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Roll No.
Total No. of Questions : 8
M.Tech. (ECE) (2018 Batch) (Sem.-2)
NEURAL NETWORKS
Subject Code : MTEC-PE3C-18
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M.Code: 76263
Time: 3 Hrs.
Max. Marks : 60
INSTRUCTIONS TO CANDIDATES :
- Attempt any FIVE questions out of EIGHT questions.
- Each question carries TWELVE marks.
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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]
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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]
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b) Define Fuzzification. Explain any one of the fuzzification scheme with an example. [06]
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]
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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]
NOTE : Disclosure of Identity by writing Mobile No. or making of passing request on any page of Answer sheet will lead to UMC against the Student.
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