<p> B.Tech IV (Fourth) Semester Examination 2015-16</p><p>Course Code: ECS405 Paper ID: 0964101</p><p>Neural Networks</p><p>Time: 3 Hours Max. Marks: 70 </p><p>Max Marks: 75</p><p>Note: Attempt six questions in all. Q. No. 1 is compulsory.</p><p>1. Answer any five of the following (limit your answer to 50 words). (4x5=20) a) List the various differences between a biological and an artificial neural network. b) List the main components of an artificial neuron. c) Explain the concept of Perceptron model along with its limitations. d) What is Hebbian Learning? Explain its importance in the field of artificial neural networks. e) Explain the concept of Linear Separability using suitable examples. f) Explain the ADALINE network briefly. g) Discuss the limitations of Associative memory networks. h) Discuss three real world applications of ANNs using suitable examples.</p><p>2. a) What are Activation Functions? Explain the various activation functions along with their descriptions. (5) b) Explain the Mathematical model of a neuron using suitable diagrams. (5) 3. a) What do you understand by Learning? Explain briefly the various learning rules. (5) b) Explain the various ANN architectures using suitable examples. (5)</p><p>4. a) Differentiate between the Discrete and Continuous Perceptron networks using suitable examples. (5) b) Explain the various Learning difficulties and their possible improvements. (5)</p><p>5. What is the generalized delta rule? Derive the Back Propagation Algorithm weight updation equation. (10)</p><p>6. a) Explain the various paradigms and the general concept of Associative Memories. (5) b) Explain the Store & Recall BAM training algorithm using some suitable example. (5)</p><p>7. What are Hopfield networks? Explain the architecture of Hopfield Networks. Also some of the salient applications of Hopfield Networks using suitable examples. (10)</p><p>8. Write short notes on any two of the following: (5+5) a) McCulloch Pitts Neuron b) Kolmogorov Theorem c) Stability Analysis of Hopfield Networks</p><p>. </p>