Download JNTUA M.Tech 1st Sem 2018 Regular Feb 9D06105 Neural Networks And Applications Question Paper

Download JNTUA (JNTU Anantapur) M.Tech ( Master of Technology) 1st Semester 2018 Regular Feb 9D06105 Neural Networks And Applications Previous Question Paper

Code: 9D06105


M.Tech I Semester Supplementary Examinations February/March 2018
NEURAL NETWORKS & APPLICATIONS
(Common to DSCE & ECE)
(For students admitted in 2012, 2013, 2014, 2015 & 2016 only)

Time: 3 hours Max. Marks: 60

Answer any FIVE questions
All questions carry equal marks
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1 (a) Compare the performance of a computer and that of a biological neural network in terms of speed of
processing, size and complexity, storage fault tolerance and control mechanism.
(b) Define activation function and describe the role of the same in the artificial neuron.
(c) Distinguish between unipolar and bipolar activation functions used in artificial neural networks giving
at least two examples of each.

2 (a) Describe McCulloch-Pitts model of a neuron. Design a network using McCulloch-Pitts neuron to
realize the NOR gate.
(b) Discuss the Widrow-Hoff rule and correlation rule.

3 (a) What is back propagation? With a schematic two-layer feed forward neural network, derive its
learning algorithm? Also discuss its learning difficulties and improvements.
(b) What is the purpose of polynomial networks?

4 (a) Discuss how the ?Winner-Take-All? in the Kohonen?s layer is implemented and explain the
architecture.
(b) What is Adaptive Resonance Theory? Explain how this theory is used to explain cluster discover
network.

5 (a) Derive expressions for the weight updation involved in counter propagation.
(b) Explain the Grossberg layer training algorithm.

6 (a) What is the Hopfield network? Describe how it can be used to have analog to digital conversion.
(b) State and prove bi-directional associative memory energy theorem.

7 (a) Describe how a neural network may be trained for a pattern recognition task. Illustrate with an
example.
(b) What are invariant characteristics of neuro computing model? Explain each of them.

8 (a) With an example, explain how neural networks help in solving travelling salesman problem?
(b) Discuss about associative memories.


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