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SATHYABAMA UNIVERSITY
(Established under section 3 of UGC Act,1956)
Course & Branch :B.Tech - IT
Title of the Paper :Soft Computing
Sub. Code :612702(2007-08)
Date :24/08/2012
Max. Marks:80
Time : 3 Hours
Session :FN
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1.
2.
PART - A
(10 x 2 = 20)
Answer ALL the Questions
List three features which distinguish different types of artificial
neural networks from each other.
Which of the following neural networks uses supervised
learning?
 Simple recurrent network.

Self-organizing feature map.

Hopfield network.

All of the above answers.
3.
Considering a graphical representation of the `tallness' of people
using its appropriate member function, which of the following
combinations are true?
(a) TALL is usually the fuzzy subset.
(b) HEIGHT is usually the fuzzy set.
(c) PEOPLE is usually the universe of discourse.
4.
Given these fuzzy graphs for member functions A and B.
Graphically show the result of the operation A OR B.
5.
If crossover between chromosomes in search space does not
produce significantly different offspring, what does it imply?
6.
The qualities of solutions offered by GAs for any problems are
always better than those provided by other search.
7.
Distinguish semantic nets from frames.
8.
How does uncertainty affect reasoning in inference chains?
9.
What sort of knowledge can be categorised as a sequence of
instructions or commands such as those presented in a cooking
recipe or motor car repair manual?
10. By what technique the facts from the knowledge base of an
expert system to prove a given goal is established?
PART – B
Answer All the Questions
(5 x 12 = 60)
11. Develop the delta learning rule for a multi-layer perceptron
(using error back-propagation), which updates the weight wji
joining neuron i to neuron j. Assume that the activation functions
in the network are continuous.
Consider cases of
o
o
j is an output neuron
j is a neuron in a hidden layer
(or)
 2

y    wk xk 
 k  0

12. An MLP is described by a single neuron as:

where x0 = 1 and w0 corresponds to the bias term. If the function
 (.) is a threshold function (min. = 0, max = 1), show
(graphically) the decision line implemented by the MLP and the
values obtained for each of the input subspaces, where: w0 = -1 ,
w1 =1 , w2 = 1
13. Discuss the ANFIS model architecture with a neat diagram.
(or)
14. Explain the operation of a Fuzzy Logic Controller with a simple
example.
15. Design a genetic algorithm to solve the any problem of your
choice, for eg., Travelling Sales Person problem. Describe
precisely the bit-string encoding and a set of crossover operators.
Then,
given a data set, discuss the procedure of using genetic
algorithm to derive the solution and also
the
stopping
criterion.
(or)
16. Consider the strings and schemata of length 11. For the following
schemata calculate the probability of surviving mutation if the
probability of mutation is 0.001 at a single bit position.
**100****10, 0**********1, 11***00***1, *1111*0000*.
Recalculate the survival probabilities for a mutation probability
Pm=0.1.
17. What is wrong with the following argument:
 Men are widely distributed over the earth.
 Socrates is a man.
 Therefore, Socrates is widely distributed over the
earth.
How should the facts be represented by these sentences be
represented in logic so that this problem does not rise?
(or)
18. Construct partitioned semantic net representation for the
following:
(a) Every batsman hit a ball.
(b) All the batsmen like the wicket-keeper.
19. How can the development of an expert system be viewed as a
core software engineering process? Illustrate with an appropriate
example to support your discussion.
(or)
20. (a) Explain the detailed life cycle model of an expert system.
(b) Discuss the features of the various components of a typical
rule-based expert system.