This document contains an exam for a neural networks and fuzzy logic control course. The exam has two parts: Part A contains 10 multiple choice questions worth 2 marks each on topics like applications of neural networks, neuron models, network architectures, and more. Part B contains two longer answer questions worth 15 marks each, asking students to describe network training algorithms, realize simple networks, and test networks on given input-output mappings. The exam was administered on February 7, 2011 to fourth year undergraduate students at K.L.N. College of Engineering in India.

1. K.L.N.COLLEGE OF ENGINEERING, POTTAPALAYAM – 630 611.
Department of Electronics And Instrumentation Engineering
Centralized Internal Test - 1
Subject : Neural Networks & Fuzzy logic control Sub. Code : IC 1451
Year/Sem : IV / VIII Academic Year : 2010-2011
Date/ duration : 07.02.11 (08:40 to 10.10 A.M) Max Marks : 50 Marks
Part –A 10 x 2 = 20 Marks
Answer all questions.
1. List two applications of artificial neural network.
2. What are the various learning rules used for training the artificial neural
network?
3. A neuron j received inputs from four other neurons whose activity levels are
10, -20, 4 & -2. The respective synaptic weights of neuron j are 0.8,0.2,-1 & -
0.9. Calculate the output of neuron j which has a linear activation function.
4. Draw Mcculloch – pits AND function neuron & OR function neuron.
5. Write the most commonly used activation function.
6. Explain the concept of linear separability.
7. Compare artificial & biological neuron.
8. Explain the function of biological neuron.
9. Sketch the architecture of perception net.
10. Draw the architecture of MADALINE.
Part – B 2 x 15 = 30 Marks
11.(a) i. Write the training algorithm for Perceptron net. (7)
ii. Realize a Hebb net for the AND function with bipolar inputs & targets
and show that it is linear separable. (8)
[OR]
(b) i. Write the training algorithm for MADALINE. (10)
ii. Develop a Perceptron net XOR function with bipolar inputs & targets. (5)
12. (a) i. Write the training algorithm for hetero associative net. (8)
ii. A hetero associative network net with the following input & target
vectors find the weight matrix and test the network. (7)
S1 (1,-1,-1,-1) t1 (1,-1) S2 (1,1,-1,-1) t2 (1,-1)
S3 (-1,-1,-1,1) t3 (-1,1) S4 (-1,-1,1,1) t4 (-1,1)
[OR]
12. (b) i. Write the training algorithm for bidirectional associative memory
neural net. (8)
ii. Consider a recurrent auto associative net used the store vector
(1,1,1,-1) determine whether it recognizes a stored vector with three
missing components. (7)
     All the Best     