Loyola College M.Sc. Computer Science April 2007 Neural Networks Question Paper PDF Download

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

M.Sc. DEGREE EXAMINATION – COMPUTER SCIENCE

HC 26

SECOND SEMESTER – APRIL 2007

CS 2952 – NEURAL NETWORKS

 

 

 

Date & Time: 21/04/2007 / 1:00 – 4:00Dept. No.                                           Max. : 100 Marks

 

 

 

PART – A

 

Answer ALL the questions                                                              5 x 2 = 10

 

  1. Define the term Neural Networks.
  2. What is called Activation of a Node?
  3. Explain Madaline in short.
  4. Write a short note on Heb Rule.
  5. What is a Maxican Hat net?
  6. What is meant by Self-Organizing Nets?
  7. Is there any difference among various versions of ART Networks?
  8. Provide a short description on Forward-only Counterpropagation.
  9. What is called White Noise and Coloured Nosie?
  10. Define the term Annealing.

 

 

PART – B

 

Answer ALL the questions                                                              5 x 8 = 40

 

 

  1. (a) Brief the Applications of ANN.

OR

(b)  Explain Perceptron  in detail.

  1. (a) Brief Bidirectional Auto-Associative Memory Nets.

OR

(b)  Explain Auto-Associative Memory Nets.

  1. (a) Explain LVQ.

OR

(b)  Explain Counterpropogation Nets.

  1. (a) Write the Algorithm for ART Networks.

OR

(b) Present the structure of ART and explain Two-Third Rule.

  1. (a) Explain Neocognitron Networks without Algorithm.

OR

(b)  Demonstrate Cascade Correlation.

 

 

 

 

 

 

 

PART – C

 

Answer any TWO questions                                                                        2 x 20  = 40

 

 

  1. (a) Brief the Mile stones in History of Artificial Neural Networks, by

highlighting on breakthroughs by scientists.

  • Write an essay on Iterative Auto-Associative Nets.
  1. (a) Briefly explain Fixed-Weight Competitive Nets.
  • Explain ART Networks.
  1. (a) Present the Backpropagation Network Architecture and explain it with the

Algorithm.

  • Write about Recurrent Nets.

 

 

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