Pune University BE (Computer Engineering) Neural Network Question Papers

B.E. (Computer) NEURAL NETWORK (2008 Pattern) (Sem.-II) (Elective-Ill)

Time :3 Hours]                                                                           [Max. Marks :100

Instructions to the candidates :

1)            Answer 3 questions from Section -1 and 3 question from Section – II.

2)            Answers to the two sections should be written in separate answer books.

3)             Neat diagrams must be drawn wherever necessary.

4)             Pigures to the right indicate full marks.

5)             Use of electronic pocket calculator is allowed.

6)            Assume suitable data, if necessary.


QIA a) Define Neural Network? Explain the characteristics of Neural Network. [8] b) Compare and contrast the biological and artificial neural network. [8]


Q2) a) What are the different models of Neuron? Explain any one in detail. [8] b) What are the basic learning laws? Explain each in brief.                                                     [8]

Q3) a) Distinguish between Learning and Training. List out steps in training algorithm. [8]

b) Explain ADALINE and MADALINE. List out some applications.               [8]


Q4) a) Explain Back-Propogation Algorithm.                                             [8]

b) What is XOR problem? Also explain how to overcome it.                              [8]

Q5) a) Describe the Boltzmann machine and Explain basic of Boltzmann learning law and it’s significance.                                                                                          [9]

b) Draw the architecture of Bidirectional Associative Memory (BAM) and explain in detail.                                                                                                                     [9]



Q6) a) Explain Auto and Hetero Associative Memory.                                       [9]

b) Draw and explain architecture of Hopfield Model.                                          [9]


Q7) a) Differentiate in between Feedback and Feed Forward Neural networks.[8] b) Explain Stochastic Process in detail.                                                                              [8]


Q8) a) Explain Stimulated Annealing algorithm and it’s structure in detail. [8]

b) Draw the architecture of a Multi Layer Perceptron (MLP) and explain its operation. Mention its advantages and disadvantages.                                          [8]

Q9) a) Distinguish between Supervised and Unsupervised Learning.                [8]

b) Draw and explain the architecture of Adaptive Resonance Theory. [8]


QIO) a) What is the purpose of Learning Vector Quantization? Explain in detail. [8]

b)            What are the different types of Hebbian Learning? Explain basic Hebbian learning. [8]

QII) a) Enlist applications of Neural Networks and Explain any two in brief. [9]

b)            What is the significance of neural networks in the NET talk application? [9]


Q22)a) Write a short note on: (any three)                                                             [18]

a)            Pattern classification.

b)            Optimization by Neural Networks.

c)            Applications in Decision Making.

d)           Associative Memories.

e)            Applications in Image Processing.

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