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Code: 9D06105
M.Tech I Semester Regular & Supplementary Examinations February 2016
NEURAL NETWORKS & APPLICATIONS
(Common to DSCE & ECE)
(For students admitted in 2011, 2012, 2013, 2014 & 2015 only)
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Time: 3 hours Max. Marks: 60
Answer any FIVE questions
All questions carry equal marks
- Draw structure and explain in detail the functions of a biological neuron. Compare and contrast the biological neuron and artificial neuron.
- Explain different ways of evaluating the performance of neural networks.
- Explain McCulloch Pitts neuron model and state its limitations.
- Distinguish between:
- (i) Supervised & unsupervised learning.
- (i) Linear separability & linearly non-separability.
- Give the details on the development of ADALINE. Explain its training algorithm.
- What is a back propagation network? Derive the expression for weight updation in a multilayer feed forward neural network using standard back propagation learning.
- What is MADALINE? Explain its training algorithm.
- A Maxnet consists of three inhibitory weights as-0.25. The net is initially activated by the input signals [0.1 0.3 0.9]. The activation function of the neuron is F(x) = {)é ft;egwise'. Find the final winning neutron.
- Give the architecture and explain the training algorithm of counter propagation networks.
- What is adaptive resonance theory (ART)? Explain how it overcomes stability plasticity dilemma in neural networks and give the significance of vigilance parameter.
- Give the architecture and explain the algorithm of Kohonen self-organizing maps.
- What are the limitations of Hopfield network? Suggest method that may overcome these limitations.
- Construct a BAM to establish the following associations between four dimensional and two dimensional patterns:
(+1, +1,-1,-1) = (+1, +1)
(+1, +1, +1, +1) — (+1, -1)--- Content provided by FirstRanker.com ---
(-1,-1,-1,-1) = (-1, +1) - What is simulated annealing? Explain in detail the method used in simulated annealing.
- Explain how neural network can be used to solve simultaneous linear equations.
- Discuss the applications of neural networks in the field of image processing.
- Discuss the use of feedback neural network to convert English text to speech.
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