Hall Ticket No Question Paper Code: BCS208
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech II Semester End Examinations (Supplementary) January, 2018
Regulation: IARE-R16
SOFT COMPUTING
(Computer Science and Engineering)
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. How neural networks differ from conventional computing? Explain in detail.
For the network shown in Figure calculate the net input to the output neuron.
Figure 1
2. Write the flowchart for the training algorithm of Hebb network or the calculation and adjustment
of weights.
Explain the Madaline architecture consisting of "n" units of input layer, "m" units of adaline
layer and "1" unit of the madaline layer.
UNIT II
3. Explain the discrete bidirectional associative memory network architecture highlighting two layers
of interaction between each other.
Explain the architecture of linear vector quantization with neat sketch.
4. Write the algorithm of discrete Hopfie/d Network.
What is Adaptive Resonance Theory Network Explain different states in clustering unit
of ART.
UNIT III
5. Explain the configuration of a pure fuzzy system.
What is the process of defuzzification? List and briefly discuss different methods of defuzzification.

Page 1 of 2
6. List and briefly discuss several ways to assign membership values to fuzzy variables in comparison
with the probability density functions to random variables.
Explain different steps used in genetic algorithm to determine the fuzzy membership function.

UNIT IV
7. Explain the set of operations that can be performed on interval analysis of uncertain values.

Briefly discuss the fuzzy propositions that make the fuzzy logic differ from classical logic.
8. Explain the algebraic properties of addition and multiplication on fuzzy numbers.
What is aggregation of fuzzy rules Explain different methods used for aggregation of fuzzy
rules.
UNIT V
9. Give the major differences exists between genetic algorithm and conventional optimisation techniques.

List and briefly discuss various selection methods available in the process of section phase of
genetic algorithm.
10. Explain different parallel genetic algorithms.
Briefly discuss different stopping condition for genetic algorithm flow.