Hall Ticket No Question Paper Code: BCS208
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech II Semester End Examinations (Regular) July, 2018
Regulation: IARE-R16
SOFT COMPUTING
Time: 3 Hours Max Marks: 70
Answer ONE Question from each Unit
All Questions Carry Equal Marks
All parts of the question must be answered in one place only
UNIT I
1. Explain the architecture of McCulloch-Pitts Neuron model.
Explain the architecture of back propagation network, depicting the direction of information flow
for the feed-forward phase.
2. Briefly discuss basic types of neuron connection architectures in artificial neural networks.
Explain different phases in error back propagation learning algorithm.
UNIT II
3. What are the activation functions used in discrete bidirectional associative memory two layer
network.
Explain the general structure of full counter propagation network and discuss different components
of instar-outstar model.
4. Write the algorithm for hetero-associative network with either noisy input or with known input.

What is learning vector quantization? Briefly discuss different variants of learning vector quantization.

UNIT III
5. Briefly discuss the properties that define classical sets and show their similarity to fuzzy sets.

List and describe the properties of Lambda-cuts for fuzzy sets.
6. List and briefly discuss several ways to assign membership values to fuzzy variables in comparison
with the probability density functions to random variables.
Write the properties of fuzzy equivalence relation. Explain each.
UNIT IV
7. List and briefly discuss the properties that the two fuzzy vectors f and both of length n.
Explain the construction and working principle of fuzzy inference systems with the help of
block diagram of FIS.
8. Give the comparison among mamdani and surgeon fuzzy interface systems.
What is fuzzy syllogism? Briefly describe different fuzzy syllogism.
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UNIT V
9. Discuss the advantages and limitations of genetic algorithms.
Briefly discuss different stopping conditions for genetic algorithm flow.
10. What is crossover in genetic algorithm? List and briefly discuss different crossover techniques.

Explain the process of converting the binary data into hexadecimal, Permutation encoding and
octal encoding.